Title :
Training the multifeedback-layer neural network using the Particle Swarm Optimization algorithm
Author :
Aksu, Inayet Ozge ; Coban, Ramazan
Author_Institution :
Dept. of Comput. Eng., Adana Sci. & Technol. Univ., Adana, Turkey
Abstract :
In this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that the MFLNN-PSO can be used for dynamic system identification as well as controller design.
Keywords :
multilayer perceptrons; particle swarm optimisation; MFLNN training; MFLNN-PSO method; chaotic time series prediction problem; controller design; multifeedback-layer neural network; nonlinear dynamic system identification; particle swarm optimization algorithm; Algorithm design and analysis; Biological neural networks; Heuristic algorithms; Neurons; Particle swarm optimization; Recurrent neural networks; Training; Multifeedback-Layer Neural Network; Particle Swarm Optimization; dynamic system identification; recurrent neural networks; training procedure;
Conference_Titel :
Electronics, Computer and Computation (ICECCO), 2013 International Conference on
Conference_Location :
Ankara
DOI :
10.1109/ICECCO.2013.6718256
