Comparison of learning algorithms for neural networks with binary inputs
Автор: Lyozin Ilya, Muravyov Vyacheslav
Журнал: Известия Самарского научного центра Российской академии наук @izvestiya-ssc
Рубрика: Информатика, вычислительная техника и управление
Статья в выпуске: 4-4 т.18, 2016 года.
Бесплатный доступ
The article compares several learning algorithms for Wang-Mendel's fuzzy neural network: steepest descent algorithm, genetic algorithm, simulated annealing, simulate particle swarm algorithm, differential evolution algorithm. The comparison has been processed on the data from the international repository with binary values. The genetic algorithm shows best results for solving the classification problem.
Learning algorithm, neural network, steepest descent algorithm, genetic algorithm, simulated annealing, simulate particle swarm algorithm, differential evolution algorithm
Короткий адрес: https://sciup.org/148204766
IDR: 148204766
