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In model based fuzzy control, almost all considered control system models are T-S and affine T-S fuzzy control systems. However, there is a lack of systematic and theoretic understanding what the similarity and difference between these two dominated fuzzy control systems models to provide the guidance to choose the right models to the right application problems. To fill in such a gap, this paper gives...
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...
This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned,...
This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able...
This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing...
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...
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