Advanced Fuzzy Systems Design and Applications by Yaochu Jin PDF

February 1, 2018 | Design | By admin | 0 Comments

By Yaochu Jin

ISBN-10: 3790817716

ISBN-13: 9783790817713

ISBN-10: 3790825204

ISBN-13: 9783790825206

Fuzzy rule structures have stumbled on quite a lot of purposes in lots of fields of technological know-how and know-how. commonly, fuzzy ideas are generated from human professional wisdom or human heuristics for really basic structures. within the previous few years, data-driven fuzzy rule iteration has been very energetic. in comparison to heuristic fuzzy ideas, fuzzy ideas generated from facts may be able to extract extra profound wisdom for extra complicated structures. This e-book offers a couple of techniques to the new release of fuzzy ideas from information, starting from the direct fuzzy inference dependent to neural web­ works and evolutionary algorithms dependent fuzzy rule new release. along with the approximation accuracy, distinctive consciousness 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 people as these generated from human heuristics. To this finish, many facets of interpretabil­ ity of fuzzy platforms were mentioned, which has to be taken into consideration within the data-driven fuzzy rule new release. during this means, fuzzy principles generated from info are intelligible to human clients and for that reason, wisdom approximately unknown platforms will be extracted.

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Consequently, the genotype defines a phenotype with a certain fitness. Whereas the genotype is a set of chromosome, the definition of phenotype is problem-related. For example, if an evolutionary algorithm is used to evolve a fuzzy system, the phenotype consists of all fuzzy rule bases that may result from the genotype. If an evolutionary algorithm is used to optimize the structure of an artificial neural network, the phenotype includes all the possible structures of the neural network. The introduction of the phenotype may contribute to a sensible analysis of evolutionary algorithms, because the analysis of genotype-fitness mapping (also known as fitness landscape) may be more difficult.

Meanwhile, it is well known that biological chromosomes are highly redundant [39]. Research in artificial evolutionary systems has also shown that redundant representation is less epistatic and makes the evolutionary algorithms mOre robust, particularly in changing environments [44, 81]. In genetic algorithms, epistasis is defined as the influence of the genotype at one locus on the effect of a mutation at another locus [232]. Alternatively, the redundancy in the genotype-phnotype mapping has also been investigated in terms of fitness landscape neutrality.

23 is the input-output surface of the designed fuzzy controller. 2 Knowledge Representation with Fuzzy Preference Models Besides IF-THEN rules, a priori knowledge can also be available that describes the relative importance of different variables or objectives, which are called preferences. Since human judgements, including preferences, are often imprecise, fuzzy logic also plays an important role in decision making and multi-criteria optimization [149). Given n alternatives Ol,02, ... 4 Knowledge Processing with Fuzzy Logic 43 -1 " Fig.

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Advanced Fuzzy Systems Design and Applications by Yaochu Jin

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