摘要
A circuit system of on\|chip BP(Back\|Propagation) learning neural network with programmable neurons has been designed,which comprises a feedforward network,a n error back\|propagation network and a weight updating circuit.It has the merit s of simplicity,programmability,speediness,low power\|consumption and high densi ty.A novel neuron circuit with programmable parameters has been proposed.It gene rates not only the sigmoidal function but also its derivative.HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1 2 μm CMOS process.The results show that both functions are matched with their res pective ideal functions very well.The non\|linear partition problem is used to v erify the operation of the network.The simulation result shows the superior perf ormance of this BP neural network with on\|chip learning.
A circuit system of on\|chip BP(Back\|Propagation) learning neural network with programmable neurons has been designed,which comprises a feedforward network,a n error back\|propagation network and a weight updating circuit.It has the merit s of simplicity,programmability,speediness,low power\|consumption and high densi ty.A novel neuron circuit with programmable parameters has been proposed.It gene rates not only the sigmoidal function but also its derivative.HSPICE simulations are done to a neuron circuit with level 47 transistor models as a standard 1 2 μm CMOS process.The results show that both functions are matched with their res pective ideal functions very well.The non\|linear partition problem is used to v erify the operation of the network.The simulation result shows the superior perf ormance of this BP neural network with on\|chip learning.
基金
Project Supported by National N atural Science Foundation of China!( U nder Grant No.696360 30 )
关键词
人工神经网络
电路设计
专用集成电路
hardware implementation of neural networks
CMOS analogue integrated circuits
p rogrammability