A multi-modal neural network using Chebyschev polynomials and its application
Ikuo Yoshihara, T. Nakagawa, M. Yasunaga, Ken Abe · 2003
This paper proposes a multi-modal neural network model composed of a pre-processing module and a post-processing module in order to enhance the nonlinear characteristics of neural networks. The pre-processing module is made of Chebyschev polynomials and transforms input data into spectra. The post-processing module is made of multilayer neural network and associates according to the spectral inputs generated by the pre-processing module. Fundamental experiments upon pattern recognition and functional approximation and experiments applying the method to a control problem result that the method enable one to build small scale neural model for nonlinear systems and to perform learning in shorter time.