Linear neural network based blind equalization
Signal Processing
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
Closed-form blind symbol estimation in digital communications
IEEE Transactions on Signal Processing
Blind channel identification and equalization withmodulation-induced cyclostationarity
IEEE Transactions on Signal Processing
A novel stochastic optimization algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolution-based design of neural fuzzy networks using self-adapting genetic parameters
IEEE Transactions on Fuzzy Systems
Blind identification and equalization based on second-order statistics: a time domain approach
IEEE Transactions on Information Theory
Adaptive Cancellation of Nonlinear Intersymbol Interference for Voiceband Data Transmission
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel
Applied Soft Computing
Conditional fuzzy clustering for blind channel equalization
Applied Soft Computing
Simultaneous batch splitting and scheduling on identical parallel production lines
Information Sciences: an International Journal
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In this paper, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated than linear channels, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented 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. In particular, we observe a relatively high accuracy and fast convergence of the method.