Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Comparing the Performance of MLP and RBF Neural Networks Employed by Negotiating Intelligent Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Symbol decision equalizer using a radial basis functions neural network
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
IEEE Transactions on Neural Networks
Complex-valued function approximation using a fully complex-valued RBF (FC-RBF) learning algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Letters: Fully complex extreme learning machine
Neurocomputing
Complex-valued support vector classifiers
Digital Signal Processing
Channel equalization using neural networks: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Complex generalized-mean neuron model and its applications
Applied Soft Computing
Adaptive filter design using recurrent cerebellar model articulation controller
IEEE Transactions on Neural Networks
Channel equalization using complex extreme learning machine with RBF kernels
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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
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A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF) networks. CMRAN has the ability to grow and prune the (complex) RBF network's hidden neurons to ensure a parsimonious network structure. The performance of the CMRAN equalizer for nonlinear channel equalization problems has been evaluated by comparing it with the functional link artificial neural network (FLANN) equalizer of J.C. Patra et al. (1999) and the Gaussian stochastic gradient (SG) RBF equalizer of I. Cha and S. Kassam (1995). The results clearly show that CMRANs performance is superior in terms of symbol error rates and network complexity