Abstract

Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. a new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. the training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). to improve the effectiveness of learning, a two-step learning approach is introduced in the training. the objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. in the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. to demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. the first example is learning a benchmark MIMO system, and the second one is the design of a wide area monitoring system for a multimachine power system. from the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach. © 2009 Elsevier Ltd.

Department(s)

Electrical and Computer Engineering

Comments

National Science Foundation, Grant 0348221

Keywords and Phrases

MIMO systems; Power system; PSO; Quantum principles; SRN; Training algorithm; Two-step learning; Wide area monitor

International Standard Serial Number (ISSN)

0893-6080

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jun 2010

PubMed ID

20071140

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