Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network
Вантажиться...
Дата
1996
Автори
DOI
Науковий ступінь
Рівень дисертації
Шифр та назва спеціальності
Рада захисту
Установа захисту
Науковий керівник
Члени комітету
Видавець
Nagoya University
Анотація
The combination of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) has already resulted in researchers advancing in quite a few real world applications but it is in control that this alliance yields much appreciable benefit. The paper reports a Radial Basis Function (RBF) network training technique which joins together global strategy of GAs and a local adjusting procedure typical for RBF networks. While activation function window centres and widths are processed via a "slow" numeric GA, output-layer neurone synaptic weights are defined by a "fast" analytical method. The technique allows to minimize not only the network hidden-layer size but also the pattern set required for training the adequate dynamical object neuroemulator.
Опис
Ключові слова
genetic algorithms, neural networks, RBF, modelling
Бібліографічний опис
Sergeev S. A. Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network / S. A. Sergeev, K. V. Mahotilo // Procs of 1st Workshop on Soft Computing WSC-1,1996, August 19-30, On the Internet, Served by Nagoya University, pp. 31-36.