Title :
Classification of polynomial-shaped measurement signals using a backpropagation neural network
Author :
Lampinen, Jouko ; Ovaska, Seppo J. ; Ugarov, Andrew
Author_Institution :
Dept. of Inf. Technol., Lappeenranta Univ. of Technol., Finland
fDate :
12/1/1994 12:00:00 AM
Abstract :
Smoothly varying signals are frequently encountered in the field of instrumentation and measurement, and they can be accurately modeled by low-order polynomials. The order identification is difficult when the measured noisy signal has frequent order variations in the underlying polynomial. In this paper, we introduce a flexible real-time order estimator, which is based on a backpropagation neural network
Keywords :
backpropagation; parameter estimation; pattern classification; polynomials; real-time systems; signal representation; delay; instrumentation; low-order polynomials; measured noisy signal; measurement; multilayer backpropagation neural network; order identification; polynomial-shaped measurement signals; real-time order estimator; smoothly varying signals; Additive noise; Backpropagation algorithms; Instruments; Integrated circuit noise; Multi-layer neural network; Neural networks; Neurons; Polynomials; Signal processing; Time measurement;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
