Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Information Sciences: an International Journal
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Channel equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
Higher-Order-Statistics-Based Radial Basis Function Networks for Signal Enhancement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A New Jacobian Matrix for Optimal Learning of Single-Layer Neural Networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
An adaptive decision feedback equalizer based on the combination of the FIR and FLNN
Digital Signal Processing
Information Sciences: an International Journal
Gaussian function assisted neural networks decoding algorithm for turbo product codes
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
Expert Systems with Applications: An International Journal
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This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.