By Yaochu Jin
Fuzzy rule structures have chanced on quite a lot of functions in lots of fields of technological know-how and expertise. routinely, fuzzy ideas are generated from human professional wisdom or human heuristics for rather easy platforms. within the previous couple of years, data-driven fuzzy rule new release has been very lively. in comparison to heuristic fuzzy ideas, fuzzy ideas generated from facts may be able to extract extra profound wisdom for extra advanced platforms. This booklet provides a few ways to the new release of fuzzy principles from info, starting from the direct fuzzy inference established to neural web works and evolutionary algorithms established fuzzy rule new release. along with the approximation accuracy, distinct awareness has been paid to the interpretabil ity of the extracted fuzzy principles. In different phrases, the bushy principles generated from information are meant to be as understandable to humans as these generated from human heuristics. To this finish, many points of interpretabil ity of fuzzy structures were mentioned, which has to be taken under consideration within the data-driven fuzzy rule new release. during this means, fuzzy principles generated from information are intelligible to human clients and as a result, wisdom approximately unknown platforms may be extracted.
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Additional resources for Advanced Fuzzy Systems Design and Applications
What is the completeness of fuzzy rule structure? A straightforward answer is that all subsets within the fuzzy partition of each input should be used by the fuzzy rule system at least by once. The most widely used full grid rule structure is complete, as shown in Fig. 19(a) . However, a complete rule structure is not necessarily a full grid rule structure. As shown in Fig. 19(b), the rule structure is complete, although there is no rule in some of the elements. 19(c) is incomplete. In fact, a compact but complete fuzzy rule structure is not only feasible, but also very essential, eespecially for large systems .
19. Rule structures. A shaded area denotes a fuzzy rule. (a) A standard grid rule structure, (b) a non-standard but complete rule structure, and (c) an incomplete rule structure. The compactness of the rule structure concerns mainly with the number of fuzzy variables that appear in the rule premises. If a fuzzy system has 10 inputs and all the inputs appear in the premises of the fuzzy rules, it is very difficult to understand the rules . One effective method for reducing the number of variables appearing in the rule premise is to optimize the rule structure with evolutionary methods [113, 126J .
1 Knowledge Representation and Acquisition with IF-THEN Rules Usually, systems that can process knowledge are called knowledge-based systems. One of the most popular and successful knowledge-based systems is the 38 1. Fuzzy Sets and Fuzzy Systems expert system. Roughly, an expert system is composed of an inference mechanism, a knowledge base, usually in the forms of rules representing specific domain knowledge, and a data base. There are several types of knowledge representation, such as the logical knowledge representation, the procedural knowledge representation, the network knowledge representation and the structured knowledge representation.