Hybrid Algorithm with Variants for Feed
Forward Neural
Network
Thinakaran Kandasamy1 and Rajasekar
Rajendran2
1Sri
Venkateswara College of Engineering and Technology, Anna University, India
2Excel Engineering
College, Anna University, India
Abstract: Levenberg-Marquardt back-propagation
algorithm, as a Feed forward Neural Network (FNN) training method, has some
limitations associated with over fitting and local optimum problems. Also Levenberg-Marquardt
back-propagation algorithm is opted only for small network. This research uses
hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) in FNN training. This algorithm includes
a number of components that gives advantage in the experimental study. Variants
such as size of the swarm, acceleration coefficients, coefficient constriction
factor and velocity of the swarm are proposed to improve convergence speed as
well as to improve accuracy. The integration of components in different ways in
hybrid algorithm produces effective optimization of back propagation algorithm.
Also, this hybrid evolutionary algorithm based on PSO can be used for complex
neural network structure.
Keywords: Back propagation, hybrid algorithm, levenberg-marquardt,
Particle swarm optimization, variants of PSO algorithm.
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