Bulletin of the Graduate School of Education, Hiroshima University. Part. II, Arts and science education Issue 55
2007-03-28 発行

Particle Swarm Optimizationを用いた階層型ニューラルネットワークの一構成

Design of a Multi-Layered Neural Network Using Particle Swarm Optimization
Kato, Mitsue
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abstract
Particle Swarm Optimization (PSO), whose concept has been established as a simulation of a simplified social milieu, is known as one of the most useful optimization methods for solving non-convex continuous optimization problems. This paper describes a new learning algorithm to simultaneously adjust connection weights included in neural networks and some user-specified parameters included in units. According to the proposed algorithm, it is possible to improve the learning properties of the neural networks, e.g., the learning cost and/or adaptability. The behavior of the proposed algorithm is examined on a numerical simulation example.
subject
neural network
evolutionary computation
learning
particle swarm optimization
meta heuristics
ニューラルネットワーク
進化計算
学習
PSO
メタヒューリスティク
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