Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

This paper studies some uncertain generalized aggregation operators based on the Shapley function, two of which are named the uncertain induced generalized Shapley averaging (UIGSA) operator and the uncertain induced generalized hybrid Shapley averaging (UIGHSA) operator. These two operators consider the interactive characteristics between elements and that of their ordered positions. Meanwhile, some important cases and desirable properties are studied. To simplify the complexity of solving a fuzzy measure, the uncertain induced generalized λ-Shapley averaging (UIGλSA) operator and the uncertain induced generalized hybrid λ-Shapley averaging (UIGHλSA) operator based on λ-fuzzy measures are presented. Finally, two new approaches to uncertain multi-attribute group decision making are developed, and a numerical example is provided to illustrate the proposed procedures.

In this paper, we investigate the multiple attribute decision making (MADM) problems with triangular fuzzy information. Motivated by the ideal of Bonferroni mean, we develop the aggregation techniques called the triangular fuzzy Bonferroni mean (TFBM) operator for aggregating the triangular fuzzy information. We study its properties and discuss its special cases. For the situations where the input arguments have different importance, we then define the triangular fuzzy weighted Bonferroni mean (TFWBM) operator, based on which we develop the procedure for multiple attribute decision making under the triangular fuzzy environments. Finally, a practical example for evaluating the modernization development of Chinese traditional medicine industry is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In today’s competitive business, supplier selection problem plays a significant role as a strategic feature in company’s success. As variety of uncontrollable and unpredictable factors affect the evaluation and decision-making process at different levels, supplier selection problem is usually very complex and unstructured, Various decision making approaches have been proposed to tackle the problem as part of a general tendering process, particularly those of multi-criteria analysis which use both quantitative and qualitative data. The aim of this paper is to integrate Fuzzy Delphi method with fuzzy analytic hierarchy process (AHP) and Fuzzy TOPSIS based approach to select the best supplier providing the highest satisfaction for the criteria determined. Fuzzy Delphi method is used to identify the most important and significant criteria then Fuzzy AHP is used to obtain the relative importance of the evaluation criteria and finally Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is used to rank the suppliers with a view to select the best one from them. In order to demonstrate the applicability of proposed approach an illustrative example is presented and the result is analyzed at the end of this paper.

In this article, a new current-mode, accurate, and fully programmable Interval Type-2 (IT2) membership function generator (MFG) in 0.18μm CMOS technology is presented. This IT2 fuzzifier is based on a Type-1(T1) fuzzifier that uses a new method for slope tuning. The proposed slope tuning method, leads to smaller active area and also significantly smaller total die area by reducing the number of required pins in comparison with previous methods. Small area, low power consumption, and especially suitable programming method, makes the proposed IT2 MFG suitable for fuzzifier block of general-purpose Fuzzy Logic Controllers (FLCs). The proposed IT2 MFG has an average power consumption of 570μW and has an area of 0.0183 mm². Simulation results using HSPICE in a typical 0.18μm CMOS technology are presented to analyze the behavior of designed circuits.

The aim of this paper is to extend the grey relational analysis (GRA) and Technique for order performance by similarity to ideal solution (TOPSIS) concepts to the fuzzy environment. Owing to vague concepts frequently represented in decision data, the crisp values are inadequate to model real-life situation. In this paper, the rating of each alternative and the weight of each criterion are described by linguistic terms which can be expressed in triangular fuzzy numbers. Then a vertex method is used to calculate the distance between two triangular fuzzy numbers. According to the concept of the GRA and TOPSIS, a fuzzy relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of fuzzy grey relational coefficient to both the fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. Finally, an example for evaluating the technical innovation capability of high technology enterprises with fuzzy information is given to show the feasibility and effectiveness of the proposed method.

System modeling is one of the most important tasks of dynamic analysis and prediction systems, and imprecise model may lead to high bias. The presence of noise in sample data can make it more difficult to obtain precise system models. A new modeling algorithm called ANFIS-GMDH is presented in this paper, which builds upon the traditional ANFIS structure and utilizes the self-organizing mechanisms of GMDH. The aim of ANFIS-GMDH is to improve upon the traditional ANFIS method and prevent overfitting of noisy data. The well-studied Box-Jenkins gas furnace data is utilized to validate the algorithm, with results showing that the proposed algorithm performs better than traditional ANFIS, GMDH and subtractive clustering for both noisy and noiseless data, without any significant increase in execution time.

This paper is devoted to study the initial and the final T-topogenous structures of the T-topogenous spaces, defined by the author in 2012. In this manuscript, we show that all initial (final) lifts in the category of T-topogenous spaces and the initial (final) T-topogenous structures exist. We introduce characterizations of the initial T-topogenous structures as special initial T-topogenous structures. The subspaces and product spaces of these T-topogenous spaces can be also characterized. Moreover, we show that the I-topology associated to the initial T-topogenous structure of a family of T-topogenous spaces coincides with the initial I-topology of the family of I-topologies associated to these T-topogenous spaces.

This paper develops a novel control approach to enhance working performance of eight-pole radial active magnetic bearings (AMB). In the proposed method, the improved fuzzy proportional-integral-derivative (PID) control based on variable universes of proportional scaling is designed firstly to obtain better identification performance and flexibility of AMB control. Then, a Kalman filter is implemented to estimate the rotor displacement to reduce the noise disturbance and undesirable parameter adjustments caused by fuzzy control scheme. Finally, the least mean square (LMS) filter is connected with fuzzy control to compensate the unknown mass unbalance which might induce serious vibrations of AMB facilities. The simulation results show that the improved fuzzy PID control plays better performance than PID control in overshoot control, and the transient time of improved fuzzy PID is much shorter compared to conventional fuzzy PID. Meanwhile, the impacts of noise disturbance can be significantly limited by connecting with Kalman filter, and the maximum steady-state error can be decreased to about 85% . It is also shown that the LMS filter algorithm can effectively compensate the unknown mass unbalance in relatively short time without affecting the stability of AMB system.

Product collaborative design scheduling problem (PCDSP) has received increasing attention in the engineering field for its popularity in manufacturing and supply chain systems. Although many scholars have achieved many useful results in Job-shop scheduling problem (JSP), the research work of PCDSP is still lacking. Moreover, the fuzziness, dynamics and multi-objective of PCD pose a great challenge for enterprises making quick response to unplanned perturbations. Thus, in this paper, multi-objective dynamic fuzzy scheduling and its solution algorithm are presented. Emergencies cases are analyzed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. The fuzziness of PCD process is studied meanwhile fuzzy triangular number and fuzzy trapezoid number are used to express the fuzziness. Based on this, a multi-objective dynamic fuzzy scheduling model aiming to minimum the makespan and tardiness penalty is constructed. As a solution, multi-objective dynamic adaptive scheduling algorithm, based on bi-layer coding strategy and self-adaptive double point crossover and self-adaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises.

Equipping NM-algebras with a Brouwer-like negation ∼, we introduce a kind of algebra under the name BZNM-algebra in the present paper and investigate its algebraic properties in detail. By means of the Brouwer-like negation ∼ and the internal Kleene negation ¬ in NM-algebras, two types of modal operators μ and ν on BZNM-algebras are defined. For any element a of BZNM-algebra, μ (a) and ν (a) turn out to be the best approximation of a from the bottom and the top, respectively, with Boolean skeleton serving as the collection of sharp elements. It is also shown that these two modal operators turn to have an S₅ behavior. Additionally, various types of sharp elements with respect to the negation operators including ∼, ¬, ♭ (an anti-intuitionistic negation) are defined, and the relationship between these sets and that of ⊗-idempotent (⊕-idempotent) elements are also investigated. It is then proved that the proposed BZNM-algebras are indeed equivalent to NM_▵ algebras under a suitable transformation of operators. Furthermore, a special kind of BZNM-algebras, i.e., BZNM³-algebras, is investigated and its equivalent characterizations are given. A new type of rough approximation operators are proposed, and it is shown that such a pair of approximation operators is equivalent to the defined operators μ and ν. Finally, two interesting examples of BZNM-algebras are presented.

This paper focuses on a risk decision making problem in high-risk work system. A special risk decision-making problem with dynamic and risky characteristics needs to be considered in work system which involves many complex factors in safety risks and control measurements. Furthermore, taking into account the inherent imprecision and uncertainty of the available data, a method based on Fuzzy Fault Tree Analysis (FFTA) is proposed to solve the problem. Unlike conventional fuzzy fault tree analysis, the fuzzy analytic hierarchy process is applied to eliminate ambiguity and subjectivity in determining the weights of criteria. Furthermore, a more simple and effective method for ranking fuzzy numbers is introduced to determine the relationships among the fuzzy results. Based on these methods, a hybrid approach is proposed for risk decision-making in accident emergency response of work system. Finally, a case study on the accident of crane hitting the high bent is given to demonstrate the proposed the validity and objectivity of the method. Compared with other available methods in the literature, the results reported here suggest that the hybrid method is more reasonable under the conditions of limited decision data and possible evolvement of emergency states.

Soft subspace clustering has attracted a lot of research efforts, due to the emergence of applications in high-dimensional clustering. To the best of our knowledge, most existing soft subspace clustering algorithms assign weights to features relying on Euclidean distance. Such schemes only allow soft subspace clustering algorithms to extend or shrink feature space with respect to feature weights. In this paper, we aim to break this limitation and facilitate feature space transformations directed by an enhanced soft subspace clustering algorithm through hybrid dissimilarity measure (ESCHD). The proposed hybrid dissimilarity measure assign weights to features based on Euclidean distance and Cosine dissimilarity, which can extend, shrink and rotate feature space so that the performance of soft subspace clustering can be improved. In ESCHD, a new optimization objective function is designed, based on the goal of minimizing the hybrid dissimilarity and maximizing the weighted entropy. Using the new objective function, we derive new updating rules for the iterations in the clustering procedure. The proved promising convergence property of ESCHD also ensures the performance of ESCHD. Experimental results on both synthetic and UCI datasets demonstrate that ESCHD is superior to the four existing clustering algorithms, i.e. K-means, W-k-means, LAC and EWKM. In addition, ESCHD is more robust to handle the problems with noise and missing data than the four algorithms.

Full implementation of the Semantic Web requires widespread availability of (fuzzy) ontologies. In this paper, we develop a complete approach and an automated tool for formalizing fuzzy object-oriented database (FOOD) models using fuzzy ontologies (i.e., constructing fuzzy ontologies from FOOD models), and investigate how to apply the constructed fuzzy ontologies to reason on FOOD models. Firstly, we propose a complete formal definition of FOOD models, which considers most features of FOOD models. On this basis, we propose an approach for constructing fuzzy ontologies from FOOD models. The approach can transform FOOD models (including schema and object instance information) into fuzzy ontologies. We also give full proof of correctness of the construction approach, and provide a full construction example. Furthermore, we implement a prototype tool called FOOD2FOnto, which can automatically construct fuzzy ontologies from FOOD models. Some tests demonstrate that our approach actually works. Finally, the constructed fuzzy ontologies are used to reason on the FOOD models, and some reasoning rules and a reasoning example are provided.

To take into account that expert’s degrees of certainty are not always comparable, researchers have used partially ordered set of degrees instead of the more traditional linearly (totally) ordered interval [0, 1]. In most cases, it is assumed that this partially ordered set is a lattice, i.e., every two elements have the greatest lower bound and the least upper bound. In this paper, we prove a theorem explaining why it is reasonable to require that the set of degrees is a lattice.

Vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Customer characteristics are neglected in traditional VRPs in the past due to the heterogeneity and ambiguousness. This study presents a vehicle route optimization model in consideration of customer characteristics with three major components: (1) A hierarchical analysis structure is developed to convert customers’ characteristics into linguistic variables, and fuzzy integration method is used to map the sub-criteria into higher hierarchical criteria based on the trapezoidal fuzzy numbers; (2) A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is proposed to group the customers into multiple clusters; (3) The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach is integrated into the dynamic programming approach to optimize vehicle routes in each cluster. A numerical case study in Anshun, China demonstrates the advantages of the proposed method by comparing with the other two prevailing algorithms. In addition, a sensitivity analysis is conducted to capture the impacts of various evaluation criteria weights. The results indicate our approach performs very well to identify similar customer groups and incorporate individual customer’s service priority into VRP.

A vague soft set is a combination of a vague set and a soft set. In this paper, we first discuss the properties of the entropy on vague soft sets. We also propose the cross-entropy of vague soft sets, and investigate the relationship between entropy and cross-entropy. Furthermore, some numerical examples are presented to illustrate the applications of the entropy and cross-entropy of vague soft sets to decision making and pattern recognition.

Healthcare data need an accurate diagnosis of diseases with the low computation time. Fuzzy association rule mining converts quantitative attributes to fuzzy attributes which maintain the integrity of information. Fuzzy association rule mining is most effective among various classification methods used for diagnosing health care data. The major challenge in the fuzzy association rule mining is to reduce the exponential growth of rules produced by fuzzy partitioning of attributes. The proposed method uses the correlation and gain ratio based average ranking feature selection followed by fuzzy weighted association rule mining classifier to diagnose the medical data set. The average ranking feature selection method improves classification accuracy and reduces the number of rules by ranking the proper potential attribute. The computation time is also minimized by reducing number of rules. The performance of fuzzy weighted association rule mining classifiers based on correlation and gain ratio average ranking feature selection is evaluated by comparing the classification accuracy with three classifiers and five benchmark data set collected from UCI repository.

Multiple features such as the margin, the shape and the texture of plant leaves are of great importance for classification of plant species, as they are often regarded as the unique features to identify plants. In this paper, we study the performance of a recently proposed semi-supervised fuzzy clustering algorithm with feature discrimination for leaf classification, based on features generated by principal component analysis of color images. The method outlines a basic framework for judging the weights of different features by adopting multiple feature matrixes obtained from the initial images as input data and the clustering results of the proposed clustering algorithm as output data to distinguish dissimilarities between various leaves. Real leaf images are employed to evaluate its performance and the experiment demonstrates that these results suggest that the margin feature, the shape feature and combination feature especially the margin feature and combination feature may be the best choice for leaf classification.

In the modern power system analysis, the investigation on the impacts of growing wind power generation can be mentioned as an inseparable part of power grids. The development of wind-thermal coordination economic dispatch leads to an optimal dispatch scheme and a mature wind power efficiency. Adding further uncertainties to the study of power systems is one of the effects of intermittent nature of wind energy which is apart from random nature of realistic power grids. Now in order to inspect these uncertainties, an efficient probabilistic method is needed to comfort the issue. In this paper a novel optimization method based on Social Spider Optimization (SSO) is presented to meet large non-convex Economic Dispatch (ED) problems and uncertainties in the network caused by wind turbines, simultaneously. To provide a more realistic model, following constrains have been included in the proposed Probabilistic ED (PED) formulation: ramp rate limits, Prohibited Operating Zones (POZs), system spinning reserve, valve loading consequences, and multiple fuel choice. Unscented Transform (UT) approach is utilized in this paper to model the uncertainty in wind speed and thereupon solve the PED. In order to show the efficiency of the presented algorithm, it is successfully tested on different systems and compared with several of the most recently published ED methods.

An efficient image encryption technique based on one-time nonlinear component for block cipher is presented in this paper. Three S-Boxes are generated by complex chaotic system, whose initial values are dependent on both the random noise and the plain image. Each S-box randomly takes turns to encrypt one of the color components in each pixel adhering to the switching sequence, which is also generated by the complex chaotic system. Subsequently, the two-stage diffusion to process images is applied. The cipher image is expanded into two composed of selected four bit-planes and diffuse them at bit-level as first stage diffusion, then reconstruct them as the input of block diffusion, which is served as second stage diffusion. The chaotic coupled map lattice employed in this method generate pseudo-random sequences in block diffusion phase. The experiment results and analysis have proved the novel image encryption method is practical and effective for encryption applications.

Identification of nonlinear systems is one of the important problems in engineering. Chaotic systems are among those nonlinear systems that have been in the center of attention of many researchers because of their complex and unpredictable behaviors. This paper proposes an algorithm for optimally estimating the parameters of system by minimizing the mean of squared errors (MSE) index. In this paper, Teaching Learning Based Optimization (TLBO) algorithm is used for solving both offline and online parameter estimation problems for chaotic systems. The validity of this algorithm in terms of convergence speed and parameter accuracy in comparison to other popular optimization algorithms such as Standard PSO (PSO), Differential Evolution (DE), Adaptive Particle Swarm Optimization (APSO) and Genetic Algorithm (GA) is shown through an illustrative example for the modeling of chaotic systems. Furthermore, in order to demonstrate the feasibility of this algorithm, it is applied to the problem of parameter identification of a well-known nonlinear Lorenz chaotic system. According to simulation results, the proposed algorithm is a very suitable algorithm for online parameter identification for a class of nonlinear chaotic systems.

Soft set theory originated by Molodtsov is an effective technique for dealing with uncertainties. In the framework of soft set theory, soft group is a key concept of algebraic theory of soft sets. The aim of this paper is provided an initial study of the linear representations of soft groups. The fundamental notions such as linear and matrix representations, equivalent representation, faithful representation, trivial representation of soft groups together with some illustrative examples are introduced. The concepts of soft invariant space, irreducible representation, completely reducible representation are proposed. And then the relationships among these concepts are demonstrated by means of several related theorems.

On residuated lattices, the notion of (∈ , ∈ ∨ q)-fuzzy t-filters is introduced and relative properties (e.g. Extension property, Triple of equivalent characteristics and Quotient characteristics) are uniformly covered by this simple general framework. The relations among (∈ , ∈ ∨ q)-fuzzy t-filters are investigated.

In this paper, hybrid technique proposed for tuning Time Delays System with proportional–integral–derivative (PID) controller. So, the performance and the robustness for a class of Time Delay System are improved. The proposed hybrid technique is the combination of the iterative algorithm and curve fitting technique. The proposed iterative algorithm is improved, performance of the feedback tuning iterative technique; so the computational complexity of the iterative algorithm reduced. By using the iterative technique, the best polynomial coefficients of curve fitting technique is determined. Using the curve fitting technique, the Time Delay System is tuned and the stability parameters of the system is maintained. The curve fitting technique is one of the non linear programming techniques which can be constructed that approximately fits the data from the extract data. The proposed hybrid technique is implemented in MATLAB working platform and the tuning performance is evaluated. Then, the system performance of the proposed hybrid technique is compared with classical PID controller, Ziegler–Nichols tuning method.

In this paper, a new order relation for ranking fuzzy numbers and the fully fuzzy linear programming (FFLP) problems with flexible constraints are proposed. Based on the new order relation, three algorithms are developed to deal with FFLP problems by converting them into some equivalent crisp linear programming problems. Algorithm 1 is employed for solving FFLP problem without flexible constraint, while Algorithms 2 and 3 are developed to deal with FFLP problem with flexible constraints. A numerical example is given to illustrate the feasibility and efficiency of our proposed algorithms.

The electronic-book (e-book) is one of the new technological changes that has significantly influenced the publishing industry in the last century. This has forced publishers to reconsider their distribution channels, since the Internet has provided a new means with which to serve readers. In this paper, a strategic market analysis is proposed from the perspective of a traditional publisher that needs to decide whether to switch to e-publishing business. The analysis model determines the publishing market equilibrium in three different market scenarios. Besides, it shows the impact of readers’ choices and price sensitivities on the publishers’ profits. The proposed decision support model has its basis on game theory and it is built in an oligopoly setting to reflect the severe market competition. The readers’ utilities and demands are modeled using the multinomial logit (MNL) model. Although the first scenario possesses a global optimum solution, in the remaining two market scenarios genetic algorithms (GAs) are used in order to find the solutions of the oligopolies. Numerical applications reveal the industry equilibrium point, where the sum of the profits of all publishers in the market is maximum.

This study investigates a novel approach to knowledge reduction in interval-valued decision formal contexts. Applying rule acquisition, we formulate a new framework of knowledge reduction for interval-valued decision formal contexts and the presented framework can be applicable to any interval-valued decision formal contexts. Based on this reduction method, more compact decision rules that can imply all of those derived from the initial interval-valued decision formal contexts in the form of implication rules are obtained. Furthermore, a corresponding reduction method is developed by constructing a discernibility matrix and its associated Boolean function.

One of the significant issues in the area of engineering is the modeling complex data with the target of prediction, classification, clustering, etc. Nevertheless, by the rapid growth of the technology, the high complexity and nonlinearity of the existing data is increased such that they put most of the traditional forecast models such as linear and nonlinear models prone to bias. In order to solve this issue, this paper suggest a newly introduced forecasting model called support vector regression (SVR) model to reach more accurate modeling and thus prediction of the complex data. In order to adjust the setting parameters of this model, the clonal selection algorithm (CSA) as a powerful optimization tool is employed. In addition, a sufficient modification is proposed to improve the search ability of this algorithm effectively. The practical test data (from 230 kV substation of Southwest Iran) are employed to demonstrate the satisfying performance of the proposed hybrid model.

This paper focuses on multi-ary α-semantic resolution automated reasoning method based on multi-ary α-resolution principle for a lattice-valued logic with truth-value in a lattice implication algebra. Firstly, the definitions of the multi-ary α-semantic resolution and multi-ary α-semantic resolution deduction in LF (X) are given, respectively, the soundness and completeness of this ground case are gotten. Secondly, the validity of multi-ary α-semantic resolution method is analyzed. Thirdly, the algorithm for multi-ary α-semantic resolution methods based on lattice-valued first-order logic LF (X) is constructed. This work will provide a theoretical foundation for the more efficient resolution based automated reasoning in lattice-valued logic.

This article suggests a probabilistic framework based on Scenario production to take account the uncertainties in the optimal operation management of Micro Grids (MGs). The MG includes different renewable energy resources such as Wind Turbine (WT), Micro Turbine (MT), Photovoltaic (PV), Fuel Cell (FC) and one battery as the storage device. The suggested framework is based on scenario generation and Roulette wheel mechanism to generate different scenarios for handling the uncertainties of different parameters. It uses normal distribution function as a probability distribution function of random parameters. The uncertainties which are considered in this paper are grid bid changes, load demand forecasting error and PV and WT output power productions. It is worthy to say that solving the MG problem for 24 hours of a day by considering different uncertainties and different constraints needs one powerful optimization method that can converge fast when it doesn’t fall in local optimum point. As a result, one Group Search Optimization (GSO) algorithm is introduced to prospect the total search space globally. The GSO algorithm is originated from group living of animals. Besides the GSO algorithm, one modification is also proposed for this algorithm. The proposed framework and method is implemented o one test grid-connected MG as a typical grid.

Li and Yang [D.F. Li and J. Yang, A difference-index based ranking bilinear programming approach to solving bimatrix games with payoffs of trapezoidal intuitionistic fuzzy numbers, Journal of Applied Mathematics 2013 (2013), 1–10] pointed out that there is no method in the literature for solving such bimatrix games in which payoffs are represented by intuitionistic fuzzy numbers and proposed a method for the same. In this paper, it is pointed out that Li and Yang have considered some mathematical incorrect assumptions in their proposed method. For resolving the shortcomings of Li and Yang’s method, a new method (named as Mehar method) is proposed. Also, the exact optimal solution of the numerical problem, solved by Li and Yang by their proposed method, is obtained by the proposed Mehar method.

Coordinate measuring machines (CMM) have a vital and enduring role in the manufacturing process because of their easy adaptation to the systems and high measurement accuracy. Owing to the demand for high accuracy and shorter cycle times of measurement tasks, determining the measurement errors has become more important in precision engineering. Additionally, manufactured components are becoming smaller and tolerances becoming tighter, and therefore, demands for accuracy are increasing. For this reason, dynamic error modeling has become a topic of considerable importance for improving measurement accuracy, manufacturing decisions and process parameter selections. A number of factors such as process parameters, measurement environment, measuring object, reference element, measurement equipment and set-up affect the measurement accuracy of CMM. Considering the complicated inter-relationships among a number of system factors, artificial intelligence-based techniques have become essential tools due to their speed, robustness and non-linear characteristics when working with high-dimensional data. In this study, a fuzzy logic-based methodology was implemented as an artificial intelligence approach for determining measurement errors related to the process parameters for coordinate measuring machines. A Mamdani-type fuzzy inference system was developed within the framework of a graphical user interface. Eight-level trapezoidal membership functions were employed for the fuzzy subsets of each model variable. The product and the centre of gravity methods were performed as the inference operator and defuzzification methods, respectively. The proposed prognostic model provided a well-suited method and produced promising results in predicting measurement errors by monitoring the process parameters such as optimum measuring point numbers, probing speed and probe radius.

Concept of soft finite state machine is being introduced, which is based on soft set theory. Concepts of soft successor and soft immediate successor are introduced and some of their properties are studied. Notions of soft subsystem, soft submachine are given here. Finally direct product of soft machines is studied.

Image Reconstruction is an important fragment in image processing. It is used to reconstruct the image which is corrupted by noise or that has some scratched regions. In order to improve the reconstruction effectiveness of the existing methods a new image reconstruction technique based on DWT and IPSO is proposed in this paper. The proposed technique is composed two main stages (i) training stage (ii) investigation stage. In training phase, initially the input cracked image is reconstructed by the DWT (Discrete wavelet Transform) method by selecting optimal threshold value using well known optimization technique as IPSO (Improved Particle Swarm Optimization). These selected threshold values are stored in the Threshold Database and they are subjugated in the image reconstruction process. In investigation stage, the threshold value is selected based on the crack level of the testing image. The proposed method is implemented in MATLAB with various cracked images. The performance of the IPSO and DWT based image reconstruction technique is checked with existing PSO and average filtering image reconstruction technique in order to prove the efficiency of the proposed method. However, our proposed methodology provides better intuitive and high-quality reconstructed image for the noisy images than the existing method, in terms of peak signal-to-noise ratio (PSNR).

In designing phase of systems, design parameters such as component reliabilities and cost are normally under uncertainties. Although there have been tremendous advances in the art and science of system evaluation, yet it is very difficult to assess these parameters with a very high accuracy or precision. Therefore, to handle this issue, this paper presents an alternative approach for solving the multi-objective reliability optimization problem by utilizing the uncertain, vague and imprecise data. For this a conflicting nature between the objectives is resolved with the help of intuitionistic fuzzy programming technique by considering the nonlinear degree of membership and non-membership functions. The resultant fuzzy multi-objective optimization problem is converted into single-objective optimization problem using the satisfaction functions with exponential weights. The optimal solution of the corresponding problem has been obtained with the cuckoo search algorithm. Finally, a numerical instance is presented to show the performance of the proposed approach.

This paper aims to incorporate a knowledge discovery technique into the Proximity-based Logic Programming paradigm in order to generate background knowledge (conceptual hierarchies) in a semi-automatic way which may lead to an efficient and desirable abstraction process among the symbols (representing concepts) from a first-order language and to the discovery of generalized relationship among them i.e. a logic-based framework with the capability of abstraction. This method makes use of the concept of λ-block characterizing the notion of equivalence when working with proximity relations. When the universe of discourse is composed of concepts which are related by proximity, the sets of λ-blocks extracted from that proximity relation can be seen as hierarchical sets of concepts grouped by abstraction level. Then, each group (forming a λ-block) can be labeled, with user help, by means of a more general descriptor in order to simulate a generalization process based on proximity. Thanks to this process, the system can learn concepts that were unknown initially and reply to queries that it was not able to answer. The novelty of this work is that it is the first time a method, with analogous features to the one aforementioned, is implemented inside a fuzzy logic programming framework. Certainly, in order to check the feasibility of the method, we have developed a software tool which have been integrated into the Bousi~Prolog system. Finally, this work presents a method to get a set of recommended abstract descriptors by using WordNet. This allows to improve the original generalization mechanism, helping the user in the task of selecting a convenient abstraction. Also, the overall method can be seen as a technique that facilitates the tuning of term ontologies.

Digital video stabilization (DVS) allows acquiring video sequences without disturbing jerkiness, removing unwanted camera movements. A good DVS should remove the unwanted camera movements while maintains the intentional camera movements. In this article, we propose a novel real-time DVS algorithm with a short-delay. The proposed DVS compensates the camera jitters applying an adaptive fuzzy filter on the global motion of video frames. The adaptive filter is an infinite impulse response (IIR) non-causal filter which is tuned by a fuzzy system adaptively to the camera motion characteristics. The fuzzy system itself is also tuned during operation according to the amount of camera jitters. The fuzzy system uses two inputs which are quantitative representations of the unwanted and the intentional camera movements. The global motion of video frames is estimated based on the block motion vectors which resulted by video encoder during motion estimation operation. Experimental results indicate a good performance for the proposed algorithm.

Interval-valued intuitionistic fuzzy numbers (IVIFNs) are usually utilized in multi-attribute decision making problems, providing an accurate description of incomplete and uncertain information. The existing IVIFN-based works mainly focus on either the prioritization relationship of attributes or both the importance and the ordered position of them. However, these aspects of attributes are all important in practical use and they should be simultaneously considered. To this end, this paper proposes an interval-valued intuitionistic fuzzy prioritized hybrid weighted aggregation (IVIF-PHWA) operator. The contribution of this paper is threefold: 1) A unit prioritized hybrid weighted aggregation (UPHWA) operator is proposed, which considers the prioritization relationship, the importance and the ordered position of attributes. 2) The UPHWA operator is extended into IVIF-PHWA operator, whose properties are all investigated. These properties illustrate that the IVIF-PHWA operator can better deal with the incomplete and imprecise information. 3) This work develops an IVIF-PHWA operator-based multi-attribute decision making method to solve the decision making problems in IVIF environment. Finally, a practical example is provided to validate the efficiency and effectiveness of the proposed approach.

In this work, we first define picture fuzzy soft sets and study some of their relevant properties, especially, a sufficient and necessary condition is presented to ensure that the dual laws are true in picture fuzzy soft theory. We then introduce an algorithm based on adjustable soft discernibility matrix by using level soft set of a picture fuzzy soft set to solve decision making problems, which can find an order relation of all the objects. Finally, an illustrative example is employed to show that they can be successfully applied to problems that contain uncertainties.

In domain theory, the lim-inf-convergence in posets was introduce to characterize continuous posets. In this paper, the concept of the L-lim-inf-convergence of nets (x _i ) _i∈I in fuzzy posets is proposed and its relationship with continuous fuzzy posets is studied. It is shown that for an arbitrary fuzzy poset, the L-lim-inf-convergence is topological if and only if the fuzzy poset is continuous.