2,979 publications from this institution
The problem of modeling dynamic systems using fuzzy relational systems is investigated. A dynamic system is modeled using a static fuzzy relational system where the dynamics is brought into the model by tapped delay lines. Both the internal and the external feedback topology are relayed. It is shown that in the limit the internal feedback topology can generate a null linguistic representation of the state in the absence of normal fuzzy sets at the inputs of the static model. For handling this several schemes are considered, namely it is advocated the use of pretuned fuzzy model I/O interfaces as regenerative devices. A case study is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Defuzzification is an important operation in the theory of fuzzy sets. It transforms a fuzzy set information into a numeric data information. This operation along with the operation of fuzzification is critical to the design of fuzzy systems as both of these operations provide nexus between the fuzzy set domain and the real-valued scalar domain. We need the synergy of both of these domains to solve many of our ill-posed problems effectively. In this paper, we address the problem of defuzzification, we present merits and demerits of various defuzzification strategies that are used in the theory and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and fuzzy inference-based systems. We also present in this paper a simple and yet a novel defuzzification mechanism. © 2001 John Wiley & Sons, Inc.
The paper introduces a design methodology for developing fuzzy systems with the aid of fuzzy JK flip-flops. The network representation of these systems expressed via basic AND and OR neurons is provided. Detailed design algorithms that essentially exploit a parametric learning of the flip-flops are studied.
The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.
This chapter begins with some methodological aspects of fuzzy sets that are of paramount relevance in the context of pattern recognition. It presents information granulation, information granules, and elaborates Granular Computing on the concept of abstraction and its role in information processing. The chapter focuses on supervised learning with fuzzy sets by showing how several main categories of classifiers are constructed by taking into consideration granular information. It also discusses unsupervised learning and shows that fuzzy sets play a dominant role given the unsupervised character of the learning processes. Fuzzy sets offer an interesting option of quantifying available domain knowledge, giving rise to an idea of partial supervision or knowledge-based clustering. The chapter highlights the list of challenges and presents selected ideas of data and feature reduction, some of the possible formulations of the associated problems and look at their solutions. Fuzzy pattern recognition comes as a coherent and diversified setting of pattern recognition.
Data mining emerges as a prudent and user — oriented sifting of data, qualitative observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system's variables. The role of fuzzy sets in knowledge discovery has not been visible even though fuzzy sets are inherently inclined towards coping with linguistic domain knowledge. This study re-examines the key issues of knowledge discovery by putting them in the context of the technology of fuzzy sets. Subsequently, we reveal several interesting conceptual and algorithmic links between linguistic data mining and fitz7y sets. The detailed investigations are geared toward inherently knowledge-oriented and context based modifications of well known techniques of fuzzy clustering.
The study is concerned with the fundamentals of granular computing and its application to neural networks. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (embracing a number of individual entitites) and their ensuing processing. We elaborate on the rationale behind granular computing.
