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Hierarchical Knowledge Bases for Fuzzy Rule-Based Systems
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In: ftp://decsai.ugr.es/pub/arai/tech_rep/ga-fl/con/ipmu00-8.ps.Z (2000)
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Techniques for learning and tuning fuzzy rule-based systems for linguistic modeling and their application
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In: http://decsai.ugr.es/~casillas/downloads/papers/casillas-cl01-academic00.pdf (1999)
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Abstract:
Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. Linguistic Mamdani-type Fuzzy Rule-Based Systems (FRBSs), the ones used to perform this task, provide a human-readable description of the model in the form of linguistic rules, which is a desirable characteristic in many problems. In this Chapter we are going to accomplish a short revision of the FRBSs where we shall see the different types that currently exist, along with their structures and characteristics, centering our attention on linguistic Mamdani-type FRBS. The performance of a linguistic FRBS depends on its Rule Base and the membership functions associated to the fuzzy partitions. Due to the complexity in the design of these components, a large quantity of automatic techniques has been proposed to put it into effect. Thereafter, we are going to review several learning (when it sets the Rule Base and sometimes the Data Base as well) and tuning (when it only sets the Data Base) methods. These methods are inspired in the three most well known approaches: ad hoc data covering, neural networks, and genetic algorithms. We shall introduce a brief description of these techniques and their synergy with FRBSs. The accuracy of the reviewed methods will be compared when solving two real-world applications. Some interesting conclusions will be obtained about the behavior of the methods, approaches, and techniques. I
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.3817
http://decsai.ugr.es/~casillas/downloads/papers/casillas-cl01-academic00.pdf
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