A quasi‐competitive network with transition between models
Toshiki Kindo · Systems and Computers in Japan · 1995
Abstract This paper proposes a quasi‐competitive network in which the mass reaction intensity of the elements in the intermediate layer is kept constant as a model that approximates the continuous nonlinear function. As the loss function, the local loss function is considered. It is composed of the error and the local model loss term reflecting the local model size. The learning algorithm of the quasi‐competitive network including the change of the number of elements is derived from the local loss function. It is shown by numerical experiment that the quasi‐competitive network can form quickly the structure reflecting the target function and has a high generalization power. It is shown also that the high generalization power is due to the constant mass reaction intensity of the elements in the intermediate layer of the quasi‐competitive network.