ON FORWARD ALGEBRA NEURAL NETWORKS FUNCTION APPROXIMATION THEORY AND LEARNING ALGORTHMS

In this paper,the MP neurons are popularized, the concepts of polynomials algebra neurons and polynomials algebra neural networks are firstly proposed, and polynomials algebra neural networks are mixed together with algebra neural networks. An analysis is made of forward algebra neural networks function approximate capability and theory foundation, and a kind of double inputs and single output four layers forward algebra neurons are designed, which can approximate a given double variable polynomials function, satisfying the given precision. A learning algorithm of algebra neural networks under p-adic is designed. This method can escape local minimum during the learning process. Finally, examples illustrate its efficiency. It is pointed out that function link artificial neural networks can be accomplished by means of activation functions of neurons, thus providing a new theory and method in approximate symbol networks computation.