【摘要】 本文提出了可构成多规则模糊神经网络的CMOS模拟单元电路,包括:类Gauss型隶属度函数电路,电压求小电路和重心算法去模糊电路.基于这些电路设计了一个两输入/一输出、25条规则的控制系统,并通过非线性函数逼近进行了验证.所有单元均采用SMIC 0.18-μm CMOS数模混合工艺制造,芯片测试结果表明:提出的单元电路结构简单,输出电压偏差小,便于扩展和调节;因而适于实现多规则,自适应调节的高速高精度控制系统.更多还原

【Abstract】 This paper proposes several improved CMOS analog circuits for neuro-fuzzy network,including Gaussian-like membership function circuit,minimization circuit,and a centroid algorithm defuzzier circuit without using division.A two-input/one-output neuro-fuzzy network composed of these circuits is implemented and testified for non-linear function approximating.All the circuits have been fabricated in SMIC 0.18-μm CMOS technology.Experiment results show that all the proposed circuits provide characteristics of high operation capacity,high speed,and simple structures.They are very suitable for rapid implementation of high-speed complex neuro-fuzzy networks.更多还原