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• W1472728679 abstract "This chapter introduces an orthogonal-transformation-based method for rule selection. This method is used in combination with the well-known backpropagation (BP) learning algorithm in neural-network modeling. Initially, an oversized fuzzy model that consists of a large number of fuzzy rules is trained using the BP algorithm. Unlike conventional methods where multiple iterations are usually required to find the “optimal” number of fuzzy rules, the proposed method is a noniterative procedure. It starts with an oversized rule base, and then removes redundant or less important fuzzy rules through a “one-pass” operation. Therefore, this method is computationally less expensive compared to the conventional methods, especially when the number of input variables is large. Moreover, this method is extremely robust numerically and can be implemented using some well-established numerical computation packages. Then singular value decomposition (SVD) and QR with column-pivoting algorithm is used to identify and remove redundant fuzzy rules. This results in a reduced fuzzy model that has a better generalizing ability. The reduced model is trained using the BP algorithm with initial parameter values determined in the oversized model. The approach is demonstrated using two nonlinear system modeling examples." @default.
• W1472728679 created "2016-06-24" @default.
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• W1472728679 date "1999-01-01" @default.
• W1472728679 modified "2023-10-16" @default.
• W1472728679 title "Identifying Fuzzy Rule-Based Models Using Orthogonal Transformation and Backpropagation" @default.
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• W1472728679 doi "https://doi.org/10.1016/b978-012443870-5.50010-1" @default.
• W1472728679 hasPublicationYear "1999" @default.
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