On Acceleration of Incremental Learning in Chaotic Neural Network

The incremental learning is a method to compose an associate memory using a chaotic neural network and provides larger capacity than correlative learning in compensation for a large amount of computation. A chaotic neuron has spatio-temporal sum in it and the temporal sum makes the learning stable to input noise. When there is no noise in input, the neuron may not need temporal sum. In this paper, to reduce the computations, a simplified network without temporal sum are introduced and investigated through the computer simulations comparing with the network as in the past. It turns out that the simplified network is able to learn input patterns quickly with the learning parameter varying.