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Hybrid simplex genetic algorithm for blind equalization using RBF networks. (English) Zbl 1012.68159
Summary: The purpose of this paper is to derive a hybrid simplex genetic algorithm for nonlinear channel blind equalization using RBF networks. Most of the algorithms for blind equalization are focused on linear channel models because of their simplicity. However, most practical channels are better approximated by nonlinear models. In order to find an effective method for nonlinear channel blind equalization, here, the equalizer based on RBF networks which is constructed from channel output states instead of the channel parameters is considered. Using the Bayesian likelihood cost function defined as the accumulation of the natural logarithm of the Bayesian decision variable, the problem becomes to maximize the Bayesian likelihood cost function with the dataset which composes the RBF equalizer’s center. For this high-dimensional complex optimal problem, the proposed hybrid simplex genetic algorithm solves it by incorporating the simplex operator with GA, and obtains a good convergence characteristic and satisfied equalization result.

68T05 Learning and adaptive systems in artificial intelligence
68W05 Nonnumerical algorithms
Full Text: DOI
[1] D.E. Goldberge, Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
[2] J.G. Proakis, Digital Communications, Fourth Edition, McGraw-Hill, New York, 2001.
[3] S. Benedetto, E. Biglieri, V. Castellani, Digital Transmission Theory, Prentice Hall, Englewood Cliffs, NJ, 1987. · Zbl 0718.94001
[4] Z. Ding, Y. Li, Blind Equalization and Identification, Marcel Dekker, New York, 2001.
[5] Falconer, D.D., Adaptive equalization of channel nonlinearities in QAM data transmission systems, Bell syst. tech. J., 47, 7, 2589-2611, (1978) · Zbl 0386.94008
[6] E. Biglieri, A. Gersho, R.D. Gitlin, T.L. Lim, Adaptive cancellation of nonlinear intersymbol interference for voiceband data transmission, IEEE J. Selected Areas Commun. SAC-2 (5) (1984) 765-777.
[7] Kaleh, G.K.; Vallet, R., Joint parameter estimation and symbol detection for linear or nonlinear unknown channels, IEEE trans. commun., 42, 7, 2406-2413, (1994) · Zbl 0801.62005
[8] H. Lin, K. Yamashita,Blind equalization using parallel Bayesian decision feedback equalizer, Mathematics and Computers in Simulation, Elsevier, Amsterdam, 2001, in press. · Zbl 0978.94019
[9] Yen, J.; Lee, B.; Randolph, D.; Liao, J.C., A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method, IEEE trans. syst. man cybernetics, 28, 2, 173-191, (1998)
[10] S. Chen; B. Mulgrew; M.P. Grant, A clustering technique for digital communication channel equalisation using radial basis function network, IEEE trans. neural networks, 4, 4, 570-579, (1993)
[11] S. Chen, G.J. Gibson, C.F.Cowan, Adaptive channel equalisation using a polynomial-perceptron structure, Proc. IEE, Vol. 137, pt.I no.5, 1990, pp. 257-264.
[12] Chen, S.; Gibson, G.J.; Cowan, C.F.; Grant, P.M., Adaptive equalization of finite non-linear channels using multilayer perceptrons, Signal process., 20, 2, 107-119, (1990)
[13] Qureshi, S.U.H., Adaptive equalization, Proc. IEEE, 73, 1349-1387, (1985)
[14] T. Stathaki, A. Scohyers, A constrained optimisation approach to the blind estimation of Volterra kernels, in: Procedings of the IEEE International Conference on ASSP 3 (1997) 2373-2376.
[15] Spendley, W.; Hext, G.R.; Himsworth, F.R., Sequential application of simplex designs in optimazation and evolutionary opreation, Technometrics, 4, 441-461, (1962) · Zbl 0121.35603
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