Title :
Differential evolution based particle swarm optimizer for neural network learning
Author :
Ning, Dongfang ; Zhang, Weiguo ; Bin Li
Author_Institution :
Coll. of Autom., Northwestern Polytech. Univ., Xi´´an
Abstract :
An improved particle swarm optimizer based on differential evolution theory is proposed. This algorithm introduces differential mutation operator into the basic particle swarm optimizer in order to solve the premature convergence problem. And this new algorithm was used to training weights and thresholds of feedforward neural network, simulation results show that this approach is effective and has an excellent convergence performance.
Keywords :
convergence; feedforward neural nets; learning (artificial intelligence); particle swarm optimisation; convergence; differential evolution; differential mutation operator; feedforward neural network; neural network learning; particle swarm optimizer; Artificial neural networks; Automation; Convergence; Educational institutions; Evolutionary computation; Feedforward neural networks; Genetic mutations; Intelligent control; Neural networks; Particle swarm optimization; Artificial neural network; Differential evolution; Particle swarm optimizer; Swarm intelligence;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4594525
