An on-chip BP learning neural network with ideal neuron characteristics and learning rate adaptation

被引：15
|
作者：
Lu, C [1 ]
Shi, BX [1 ]
Chen, L [1 ]
机构：
[1] Tsinghua Univ, Inst Microelect, Beijing 100084, Peoples R China
来源：
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING | 2002年 / 31卷 / 01期
基金：
中国国家自然科学基金;
关键词：
circuit design; hardware implementation; neural network; on-chip BP learning;
D O I：
10.1023/A:1014476806076
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 mum 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.
引用
收藏
页码：55 / 62
页数：8
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