By Yaochu Jin
Fuzzy rule structures have chanced on a variety of purposes in lots of fields of technological know-how and expertise. frequently, fuzzy principles are generated from human specialist wisdom or human heuristics for quite basic platforms. within the previous couple of years, data-driven fuzzy rule iteration has been very lively. in comparison to heuristic fuzzy ideas, fuzzy principles generated from facts may be able to extract extra profound wisdom for extra complicated platforms. This booklet provides a few methods to the iteration of fuzzy principles from information, starting from the direct fuzzy inference established to neural internet works and evolutionary algorithms established fuzzy rule new release. in addition to the approximation accuracy, particular realization has been paid to the interpretabil ity of the extracted fuzzy ideas. In different phrases, the bushy ideas generated from information are meant to be as understandable to humans as these generated from human heuristics. To this finish, many facets of interpretabil ity of fuzzy structures were mentioned, which needs to be taken into consideration within the data-driven fuzzy rule iteration. during this means, fuzzy ideas generated from facts are intelligible to human clients and for that reason, wisdom approximately unknown platforms could be extracted.
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Extra resources for Advanced Fuzzy Systems Design and Applications
Traffic regulations and signs. These include the speed limit and other traffic signs such as stop, yield, traffic light and so on. The goal of the fuzzy system is to control the car properly so that it is able to follow the car ahead of you properly. For the sake of simplicity, we only consider the two most essential factors, namely, the safety distance and the relative speed in designing the fuzzy rule system. The design procedure can generally be divided into the following steps: 1. Determination of the input and the output.
15. Repairing of an incomplete fuzzy partition . • Interpolation. Suppose a fuzzy rule base has the following two rules: R I : If x is AI, then y is B I ; R 2 : If x is A 2 , then y is B 2 . The fuzzy membership functions are illustrated in Fig. 16(a) and (b) . To make the rule base complete, a third fuzzy rule R3: If x is AI, then y is BI can be generated. The key issue is how to determine parameters of the fuzzy membership functions AI and BI. A general condition that the fuzzy rules are consistent.
However, if we rewrite the rules as: R l : If x is positive Small, Then y is positive Large; R 2 : If x is positive Medium, Then y is positive Medium. If the premise parts of two fuzzy rules are completely different, then it is hard to say that they are inconsistent, no matter what consequents of the two rules are. For example: R l : If x is positive Large, Then y is positive Small; R 2 : If x is negative Large, Then y is positive Small. In these two rules, their premise parts are completely different, although their consequent is the same.
Advanced Fuzzy Systems Design and Applications by Yaochu Jin