On simplification of chaotic neural network on incremental learning
Toshinori Deguchi, Toshiki Takahashi, Naohiro Ishii · 2014
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 spatiotemporal 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 has the same capacity to and can learn faster than the usual network.