Convergence and limit points of neural network and its application to pattern recognition

A novel neural network model, based on the gradient system theory, is introduced. The proposed design approach solves the problem of parasitic limit points. This could have significant impact on many potential applications, particularly in the area of pattern classification/recognition. The design approach, the development of the Lyapunov function, the stability analysis, and the convergence characteristics of the neural network are discussed in detail. Design examples and simulation results are presented to illustrate the design process and the convergence characteristics of the proposed neural network. One example shows its application in pattern recognition