Adaptive training of artificial neural network

Author

Khaparde, S.A. ; Parnerkar, A. ; Hiremath, N.S. ; Sheshaprasad, B.J.

Author_Institution

Indian Inst. of Technol., Bombay, India

fYear

1992

Firstpage

525

Abstract

Adaptive training of a neural network for nonstationary processes is reported within the framework of a multilayer perceptron model using the backpropagation (BP) algorithm. The error introduced by small changes in system parameters is reflected to adapt the changes in the converged weight matrix. The error is minimized using a constrained optimization method like the gradient projection method (GPM). The method is applied for harmonic prediction in voltage waveforms. The results for a sample system are discussed

Keywords

backpropagation; feedforward neural nets; optimisation; power system harmonics; adaptive training; artificial neural network; backpropagation; constrained optimization method; converged weight matrix; gradient projection method; harmonic prediction; multilayer perceptron model; nonstationary processes; voltage waveforms; Artificial neural networks; Australia; Educational institutions; Gradient methods; Load forecasting; Multi-layer neural network; Multilayer perceptrons; Neural networks; Optimization methods; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '92. ''Technology Enabling Tomorrow : Computers, Communications and Automation towards the 21st Century.' 1992 IEEE Region 10 International Conference.

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-0849-2

Type

conf

DOI

10.1109/TENCON.1992.272004

Filename

272004