Weights and structure determination of Chebyshev-polynomial neural networks for pattern classification

This paper firstly proposes a new type of single-output Chebyshev-polynomial feed-forward neural network (SOCPNN) for pattern classification. Then, based on such a SOCPNN, another new type of multi-output Chebyshev-polynomial feed-forward neural network (MOCPNN) is proposed for pattern classification in this paper. In order to avoid lengthy iterative-learning procedure, the weights-direct-determination (WDD) method is applied to obtaining the optimal weights of the SOCPNN and MOCPNN. In addition, the 4-fold cross-validation (4FCV) method is used to determine the appropriate numbers of the SOCPNN and MOCPNN hidden-layer neurons such that they can achieve good performances (i.e., approximation and generalization). By combining the presented WDD with 4FCV methods, two weights-and-structure-determination (WASD) algorithms, one for the SOCPNN and the other for the MOCPNN, are thus proposed for pattern classification. Furthermore, experiment results substantiate the high accuracy and strong robustness of the proposed SOCPNN and MOCPNN equipped with the WASD algorithms for pattern classification.