Self-tuning neural controller design with orthonormal activation functions

摘要

This paper presents an attractive artificial neural network architecture with orthonormal type of activatin functions (e.g. harmonic, Legendre, laguerre and Chebychev) for modeling and self-tuning control of nonlinear proceses. An algorithm for modeling and self-tuning control of uncertain nonlinear complex processes is proposed. Several types of orthonormal functions for nonlinear process modeling and control are analyzed and compared. Simulation results are provided to illustrate high performance of artificial neural networks with the orthonormal activation functions in practical industrial problems. The obtained results are compared and potential shortcomings of the proposed methods for real time industry control are discussed and analyzed.