An On-Chip BP Learning Neural Network with Ideal Neuron Characteristics and Learning Rate Adaptation

Abstract

An on-chip BP(Back-Propagation) learning neural network with ideal neuron characteristics and learning rate adaptation is designed. A prototype LSI has been fabricated with a 1.2 μm CMOS double-poly double-metal technology. A novel neuron circuit with ideal characteristics and programmable parameters is proposed. It can generate not only the sigmoid function but also its derivative. The test results of this neuron circuit show that both functions match with their ideal values very accurately. A learning rate adaptation circuit is also presented to accelerate the convergence speed. The 2-D binary classification and \sin(x) function fitness experiments are done to the chip. Both experiments verify the superior performance of this BP neural network with on-chip learning.