Comparative study between functions distributed network and ordinary neural network

A functions distributed network, called universal learning networks with branch control of relative strength (ULNs with BR), is proposed. The point of the paper is to adjust the outputs of the intermediate nodes of the basic network using an additional branch control network. The adjustment multiplies the nodes outputs by the coefficients ranging from zero to one, which is obtained from the branch control network. Therefore, the following are characterized in ULNs with BR, (1) the branch is cut when the coefficient of its branch is zero, and (2) multiplication is carried out in the nodes outputs adjustment when the coefficient takes a nonzero value. ULNs with BR is applied to two-spirals problem. The simulation results show that ULNs with BR exhibits better performance than the conventional neural networks with comparable complexity.