この文献の参照には次のURLをご利用ください : https://doi.org/10.15027/18310
広島大学大学院教育学研究科紀要. 第二部, 文化教育開発関連領域 Issue 55
published_at 2007-03-28
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
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.
About This Article
Keywords
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
Other Article
PP. 197 - 206