Proceedings Article10.1109/CICA.2009.4982784
Learning functions generated by randomly initialized MLPs and SRNs
Ryan Cleaver,Ganesh K. Venayagamoorthy +1 more
- 27 May 2009
- pp 62-69
TL;DR: In this article, a particle swarm optimization (PSO) algorithm with a quantum step utilizing the probability density property of a quantum particle is proposed. And the results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms.
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Abstract: In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but PSO-QI provides learning capabilities of these functions by MLPs and SRNs compared to BP and PSO.
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Citations
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TL;DR: This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date and presents an overview on applications of QCI in solving various problems in engineering.
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Andries P. Engelbrecht
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TL;DR: Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.
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Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
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