Knowledge-increasable artificial neural network and natural gradient algorithm

Provides a knowledge-increasable artificial neural network model and learns parameters by using a probability model. A conventional gradient algorithm is normally adopted to learn parameters in a KI network, but its performance isn´t the best. The paper uses the natural gradient algorithm that takes the Riemannian metric of parameter space to define the parameters. This method can adaptively modify the parameters based on a Riemannian metric and achieve the approximate best performance