FPGA implementation of neural network for linearization of thermistor characteristics

Author

Sonowa, Durlav ; Bhuyan, Manabendra

Author_Institution

Dept. of Electron. & Commun. Eng., Tezpur Univ., Tezpur, India

fYear

2012

fDate

15-16 March 2012

Firstpage

422

Lastpage

426

Abstract

This paper presents an FPGA (Field Programmable Gate Array) implementation of an artificial neural network (ANN) for linearization of nonlinear characteristics of a thermistor. A feed forward ANN is used for linearization. The network is trained in MATLAB with back propagation algorithm; weights and biases are determined and then implemented in Spartan-III FPGA. Subroutines are developed for single precision floating point arithmetic in IEEE-754 format.

Keywords

IEEE standards; backpropagation; electronic engineering computing; feedforward neural nets; field programmable gate arrays; floating point arithmetic; mathematics computing; thermistors; IEEE-754 format; MATLAB; Spartan-Ill FPGA; artificial neural network; backpropagation algorithm; biases; feedforward ANN; field programmable gate array; nonlinear characteristics; single precision floating point arithmetic; thermistor characteristics linearization; weights; Algorithms; Artificial neural networks; Backpropagation; Field programmable gate arrays; Neurons; Silicon; Table lookup; FPGA implementation of ANN; Floating Point FPGA; Sensor linearization;

fLanguage

English

Publisher

ieee

Conference_Titel

Devices, Circuits and Systems (ICDCS), 2012 International Conference on

Conference_Location

Coimbatore

Print_ISBN

978-1-4577-1545-7

Type

conf

DOI

10.1109/ICDCSyst.2012.6188753

Filename

6188753