Structure and Parameter Learning of Neuro-Fuzzy Systems: A Methodology and a Comparative Study
Authors
Abstract
A methodology and experimental comparison of neuro-fuzzy structures, namely linguistic and zero and first-order Takagi-Sugeno, are developed. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase, the structure of the model is obtained by subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. Membership functions with two-sided Gaussian functions are proposed and discussed. In the second phase, the modelparameters are tuned via the training of a neural network. Furthermore, different fuzzy operators are compared, as well as regular and two-sided Gaussian functions.
Keywords
Nero-fuzzy systems, structure learning, parameter learning, clusteringSubject
Neuro-Fuzzy ModellingJournal
Journal of Intelligent and Fuzzy Systems, Vol. 11, #3, pp. 147-161, IOS Press, December 2001PDF File
Cited by
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S Gharehbaghi, M Khatibinia - Earthquake Engineering and Engineering …, 2015 - Springer
Abstract A reliable seismic-resistant design of structures is achieved in accordance with the
seismic design codes by designing structures under seven or more pairs of earthquake
records. Based on the recommendations of seismic design codes, the average time-
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