Self-organizing network for optimum supervised learning

Abstract

A new algorithm called the self-organizing neural network (SONN) is introduced. Its use is demonstrated in a system identification task. The algorithm constructs a network, chooses the node functions, and adjusts the weights. It is compared to the backpropagation algorithm in the identification of the chaotic time series. The results show that SONN constructs a simpler, more accurate model, requiring less training data and fewer epochs. The algorithm can also be applied as a classifier.