The Conjugate Gradient Optimum Algorithm of Multi-Layers Neural Network and its Application in Pattern Recognition

Abstract

Defining network average error as optimum objective function, weights and thresholds as design variables,a new kind of real conjugate gradient optimum algorithm were studied. The method overcomes the oscillation phenomenon. The best step length can be aquired per compution. The objective function decreases gradually. A computing program about weights and threshold, based on accurate conjugate gradient optimum algorithm of multi layer neural network, was put forward. The selecting method of rational construct was pointed out. Through analyzing neural network of status of soccer robots and pattern recognition, its validity and applying prospect was showed.