Convergence Analysis of Multi-innovation Learning Algorithm Based on PID Neural Network

Convergence Analysis of Multi-innovation Learning Algorithm Based on PID Neural Network

In order to improve the identification accuracy of dynamic system, multi-innovation learning algorithm based on PID neural networks is presented, which can improve the online identification performance of the networks. The multi-innovation gradient type algorithms use the current data and the past d...

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Bibliographic Details
Main Authors: Gang Ren, Pinle Qin, Minmin Sun, Yan Lin
Format: Article
Language:English
Published: IFSA Publishing, S.L. 2013-05-01
Series:Sensors & Transducers
Subjects:
Multi-innovation
PID neural networks
System identification
Nonlinear system
Online Access:http://www.sensorsportal.com/HTML/DIGEST/may_2013/Special_issue/P_SI_356.pdf
Description
Summary:In order to improve the identification accuracy of dynamic system, multi-innovation learning algorithm based on PID neural networks is presented, which can improve the online identification performance of the networks. The multi-innovation gradient type algorithms use the current data and the past data that make it more effective than the BP algorithm in view of accuracy and convergence rate. Simulation results showed that the proposed algorithm is effect.
ISSN:2306-8515
1726-5479

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