Neural networks with functions of synaptic weights and its application to nonlinear system control

In this paper, a new method for faster neural networks learning is proposed. The characteristic of our method is that the neural networks have functions of synaptic weights instead of synaptic weights in order to improve the sensitivity of the criterion functions with respect to the synaptic weights. By constructing the functions of synaptic weights appropriately, the learning process can be significantly improved. By a simulation study of learning of controller parameters for a nonlinear crane system control, it is clarified that the speed of learning by the proposed method is much faster than that of the conventional method