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

In many expositions of fuzzy methods, fuzzy techniques are described as an alternative to a more traditional statistical approach. In this paper, we present a class of real-life situations in which fuzzy techniques can be naturally reformulated in statistical terms: namely, in terms of interval-valued statistics. We hope that this reformation will make the corresponding fuzzy techniques more acceptable to researchers whose only experience is in using traditional statistical methods.

The fuzzy extension principle has been widely used to extend the domain of mathematical functions and relations from elements of a referential set to fuzzy subsets of that referential set. However, there are restrictions associated with the fuzzy extension principle. This paper addresses the question how restricted is the family of "fuzzy set to fuzzy set mappings" obtained by the fuzzy extension of non-fuzzy functions and relations, as compared to the general family of all possible fuzzy set to fuzzy set mappings. A theorem is presented, with the necessary and sufficient conditions, to determine this restriction for the fuzzy extension of non-fuzzy relations and point-valued functions. It is shown that the fuzzy extension principle would impose a restriction on extended fuzzy set to fuzzy set mappings, which is similar to the linear restriction for point-valued functions. Moreover, the extension of mappings from a set-valued domain to a fuzzy set-valued domain is discussed. It is shown that this extension is well-behaved only for those mappings which preserve subsethood order. Another theorem with the necessary and sufficient condition has been proved to determine the imposed restriction during the fuzzy extension of subsethood order preserving set to set mappings. The two extensions have been compared and several examples are provided.

Data warehousing is emerging as one of the hottest areas of growth in global business and the critical component and tool for business intelligence. In the data warehouse environment, the level of summarization plays an important role. Some Data Warehouse applications, directly account for the uncertainty and vagueness often associated with time-series and enterprise-wide decision making. In this paper, we enhance the concept of summarization using fuzzy technology, keeping in view the time-series and enterprise-wide data. This enhancement, which extends the aggregation process used in data warehouse, provides insightful information intelligently and allows greater facility for modeling human decision making.

In this paper, two approaches to reduce the damage caused by the mechanical vibrations in machines are presented. In the first approach, an adaptive notch filter is employed to detect online the resonant frequencies in the vibration signal and to adaptively align its notch to prevent signals at these frequencies from being propagated and amplified in the control loop. In the second approach, a real-time analyzer, based on a fuzzy fusion technique, is used to continuously monitor and compare the vibrational pattern against a vibration signature. The vibration signal can be acquired using an accelerometer mounted on the machine. As such, it can be applied to existing machines without modifications. Simulation and experimental results are provided to highlight the advantages associated with these approaches.

The application of Fuzzy Logic (FL) in traditional fields such as engineering, medicine or finance has become a common practice. However, in social sciences (mainly politics), the use of FL can still be intensely explored. The complex and uncertain criteria of political problems can be investigated in a clearer way joining fuzzy with Multiple Criteria Decision Making (MCDM). The practical result of this research is the creation of a computational system for election forecasting which uses linguistic terms to represent the system inputs and criteria weights. A flexible system is proposed in which the function format of the fuzzy criteria weights and of the fuzzy rating criteria can be chosen by the system user (the Decision Maker) according to particular needs. In addition, a new perspective to ideal positive and ideal negative concepts using fuzzy LR intervals is also proposed.

Fuzzy systems are currently finding practical applications, ranging from "soft" regulatory control in consumer products to accurate modelling of non-linear systems. This paper presents the design of a classification system for vehicle acoustic signal classification. Traffic management and information systems rely on a suite of sensors for estimating traffic parameters. Currently inductive loop detectors and video-based systems are often used to count and detect vehicles. Loop detectors are expensive to maintain and video-based systems are sensitive to environmental conditions and do not perform well in vehicle classification. Vehicle classification is important in the computation of the percentages of vehicle classes that use streets and motorways. The use of an automated system can lead to adequate road surface maintenance with obvious results in cost and quality. However the sound of a working vehicle could provide an important clue to the vehicle type. A novel approach, based on adaptive fuzzy logic systems, has been discussed in this paper. Its performance is evaluated through a simulation study, using metered data collected from a roadside microphone-array sensor at the Valle d'Aosta highway in north-western Italy. The results indicate that the fuzzy classifier based on the proposed defuzzification method, namely area of balance (AOB), provide more accurate classifications compared to other classifiers.

In this study, a hybrid and intelligent structure that blends the "PI-type" and "PD-type" output portions of a new fuzzy logic controller is presented. The new fuzzy logic controller consists of two rule-base blocks and a logical switch in between and its rule-base is formed using new meta-rules. The rule-base blocks admit two inputs one of which is newly devised and called "normalized acceleration" and the other one is the classical "error". The newly devised input gives a relative value about the "fastness" or "slowness" of the system response and provides an easy and straightforward way of forming the rule-base. The new hybrid controller structure is formed in such a way that it intelligently blends PI and PD portions of the fuzzy logic controller through a new empirical relation or a rule-base. Both the empirical relation and the rule-base use the same inputs of the new fuzzy logic controller. The robustness and effectiveness of the new fuzzy logic controller with/without the blending mechanism are illustrated through simulations done on a second-order system with varying parameters. Furthermore, the results of the new hybrid fuzzy PID controller is compared with the results obtained using both the conventional fuzzy PID controller and another hybrid method which is an augmented version of fuzzy PI controller with a resetting factor.