DocumentCode :
2590273
Title :
Improved Opposition-Based PSO for Feedforward Neural Network Training
Author :
Rashid, Muhammad ; Baig, Abdul Rauf
Author_Institution :
Nat. Univ. of Comput. & Emerging Sci., Islamabad, Pakistan
fYear :
2010
fDate :
21-23 April 2010
Firstpage :
1
Lastpage :
6
Abstract :
In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.
Keywords :
feedforward neural nets; particle swarm optimisation; feedforward neural network; opposition-based generation jumping; opposition-based velocity calculation; particle swarm optimization; Clamps; Cognition; Computer networks; Convergence; Cultural differences; Equations; Feedforward neural networks; Neural networks; Particle swarm optimization; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Applications (ICISA), 2010 International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-5941-4
Electronic_ISBN :
978-1-4244-5943-8
Type :
conf
DOI :
10.1109/ICISA.2010.5480380
Filename :
5480380
Link To Document :
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