Title

Comparison of Quantum-inspired Evolutionary Algorithm and Particle Swarm Optimization for Neural Network Training

Abstract

Neural Networks (NN) are known to be unversal approximators for any non-linear function. Training algorithms are critical when neural networks are applied to high speed applications with complex nonlinearities. The performance of training algorithm varies depending on the type of neural network. Quantum-Inspired Evolutionary Algorithm (QEA) is proposed as a novel technique for NN training, which is based on the concept and principles of quantum computing. QEA utilizes the concepts of superposition of states to do parallel processing on all the possible solutions. To demonstrate its effectiveness, studies are carried out for training feedforward neural networks (FFNNs) to learn time-independent and time-dependent nonlinear functions. The training with QEA has been compared with Binary Particle Swarm Optimization (BPSO) algorithm in this paper.

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)

Keywords and Phrases

Neural Networks; Particle Swarm Optimization; Quantum Evolutionary Algorithm

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2004 Knowledge Engineering and Discovery Research Institute, All rights reserved.

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