Analogue circuit realization of a programmable sigmoidal function and its derivative for on-chip BP learning

Author

Lu, Chun ; Shi, Bingxue ; Chen, Lu

Author_Institution

Inst. of Microelectron., Tsinghua Univ., Beijing, China

fYear

2000

fDate

2000

Firstpage

626

Lastpage

629

Abstract

In an on-chip Back-Propagation (BP) algorithm learning neuron, both the sigmoidal activation function and its derivative are needed. A novel analog circuit is proposed, which can realize both functions. The neuron can be adapted to various environments by programming the threshold and the gain factor of the sigmoidal function. The nonlinear partition problem is used to verify the operation of the proposed circuit

Keywords

CMOS analogue integrated circuits; analogue processing circuits; backpropagation; neural chips; programmable circuits; ANN; analogue circuit realization; backpropagation algorithm; nonlinear partition problem; onchip BP learning neuron; programmable gain factor; programmable sigmoidal function; programmable threshold; sigmoidal activation function; sigmoidal activation function derivative; Analog circuits; Artificial neural networks; Concurrent computing; Functional programming; Microelectronics; Network-on-a-chip; Neural networks; Neurons; Partitioning algorithms; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on

Conference_Location

Tianjin

Print_ISBN

0-7803-6253-5

Type

conf

DOI

10.1109/APCCAS.2000.913579

Filename

913579