Design of neural networks with the hidden-layer control part and memory part

In this paper, a neural network model, which easily controls weights and unit offsets, is proposed. It consists of multilayer neural networks with a control part and an address memory part. The control part controls weights between the input layer and the hidden layer and unit offsets of the hidden layer, and sends the output data to the address memory part. The address memory part memorizes the output pattern of the hidden layer, which is compared with the input pattern, and sends the learning data to the output layer after learning. Simulation results show that the weights control and unit offsets control between layers are easy, convergence speed is fast, and it does not fall into the local minima during learning.