Convergence Analysis of Inverse Iterative Neural Networks with L2 Penalty

Abstract

Abstract The iterative inversion of neural networks has been used in solving problems of adaptive control due to its good performance of information processing. In this paper an iterative inversion neural network with L2 penalty term has been presented trained by using the classical gradient descent method. We mainly focus on the theoretical analysis of this proposed algorithm such as monotonicity of error function, boundedness of input sequences and weak (strong) convergence behavior. For bounded property of inputs, we rigorously proved that the feasible solutions of input are restricted in a measurable field. The weak convergence means that the gradient of error function with respect to input tends to zero as the iterations go to infinity while the strong convergence stands for the iterative sequence of input vectors convergence to a fixed optimal point.