A Hybrid Genetic Algorithm with Simulated Annealing for Nonlinear Blind Equalization Using RBF Networks

Authors:
Soowhan Han;Imgeun Lee;Changwook Han
Affiliations:
Department of Multimedia Engineering, Dongeui University, Busan,614-714, Korea;Department of Multimedia Engineering, Dongeui University, Busan,614-714, Korea;School of Electrical Eng. and Computer Science, Yeungnam University,712-749, Korea
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Year:
2007

Citing 7
Cited 0

Fuzzy techniques for adaptive nonlinear equalization

Signal Processing - Special issue on fuzzy logic in signal processing
Reconstructing occlusal surfaces of teeth using a genetic algorithm with simulated annealing type selection

Proceedings of the sixth ACM symposium on Solid modeling and applications
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid simplex genetic algorithm for blind equalization using RBF networks

Mathematics and Computers in Simulation
A Constrained Optimisation Approach To The Blind Estimation Of Volterra Kernels

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Channel equalization using radial basis function network

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
A novel stochastic optimization algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is derived for nonlinear channel blind equalization using RBF networks. The proposed hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. It is shown that the relatively high accuracy and fast convergence speed have been achieved.