A functional neural fuzzy network for classification applications

Authors:
Chi-Feng Wu;Cheng-Jian Lin;Chi-Yung Lee
Affiliations:
Department of Information Management and Communication, Wenzao Ursuline College of Languages, Kaohsiung City 807, Taiwan, ROC;Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung County 411, Taiwan, ROC;Department of Computer Science and Information Engineering, NanKai University of Technology, Nantou County 542, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This study presents a functional neural fuzzy network (FNFN) for classification applications. The proposed FNFN model adopts a functional neural network (FLNN) to the consequent part of the fuzzy rules. Orthogonal polynomials and linearly independent functions are used for a functional expansion of the FLNN. Thus, the consequent part of the proposed FNFN model is a nonlinear combination of input variables. The FNFN model can construct its structure and adapt its free parameters with online learning algorithms, which consist of structure learning algorithm and parameter learning algorithm. The structure learning algorithm is based on the entropy measure to determine the number of fuzzy rules. The parameter learning algorithm, based on the gradient descent method, can adjust the shapes of the membership functions and the corresponding weights of the FLNN. Finally, the FNFN model is applied to various simulations. The simulation results for the Iris, Wisconsin breast cancer, and wine classifications show that FNFN model has superior performance than other models for classification applications.