Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network
Abstract: Neural network is
one of the branches with the most active research, development and application
in computational intelligence and machine study. Radial basis function neural
network (RBFNN) has achieved some success in more than one application field,
especially in pattern recognition and functional approximation. Due to its
simple structure, fast training speed and excellent generalization ability, it has
been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm
intelligent optimization algorithm derived from the study on the preying
behavior of fish swarm. This algorithm is not sensitive to the initial value
and the parameter selection, but strong in robustness and simple and easy to
realize and it also has parallel processing capability and global searching
ability. This paper mainly researches the weight and threshold of AFSA in
optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is
significantly advantageous in global optimization capability and that it has
outstanding global optimization ability and stability.
Author: Yuhong Zhou, Jiguang
Duan, Limin Shao
Journal Code: jptkomputergg160226
