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This paper presents a structure learning method for fuzzy systems following our previous work on a Structure Evolving Learning Method for Fuzzy Systems (SELM) and an Evolving Construction Scheme for Fuzzy Systems (ECSFS). Here we extend our previous work to a structure learning method for fuzzy systems which results in more concise systems. We analyse and compare the proposed concise structure learning...
When applying gradient descent learning methods to hierarchical fuzzy systems, there is great difficulty in handling the intermediate variables introduced by the hierarchical structures, as the intermediate variables may go outside their definition domain that makes gradient descent learning invalid. To overcome this difficulty, this paper proposes a learning scheme that integrates a normalization...
This paper proposes an incremental construction learning algorithm for identification of T-S fuzzy Systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. Beginning with a simplest T-S fuzzy system, the algorithm develops the system structure by adding more fuzzy terms and rules to reduce the model errors in a dasiagreedypsila way. The main features of the proposed...
Standard fuzzy systems suffer the "curse of dimensionality" which has become the bottleneck when applying fuzzy systems to solve complex and high dimensional application problems. This curse of dimensionality results in a larger number of fuzzy rules which reduces the transparency of fuzzy systems. Furthermore too many rules also reduce the generalization capability of fuzzy systems. Hierarchical...
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