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Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximar tors. Neural Networks, 1989, NN-2(2): 359-366.[2]Par J, Sandberg I W. Universal approximation using radial-based-function networks[J].Neural Computation.1991, 3:246-257[3]Zhang Q H, Benvenisete A. Wavelet networks. IEEE Trans. on Neural networks, 1992, NN-3(6): 889-989.[4]Zhang J, Walter G G, Miao Y B, Lee W N. Wavelet neural networks for function learning[J].IEEE Trans. on Signal Processing.1995, 43(6):1485-1496[5]Kreinovich V, Sirisaengtaksin V, Cabrea S. Wavelet neural networks are optimal approximators for functions of one variable. University of Texas at EL. Paso, Computer Science Department Technical Report, 1992, No. UTEP-cs-92-29.[6]Delyon B, Juditsky A, Benveniste B. Accuracy analysis for wavelet approximation. IEEE Trans. on Neural Networks, 1995, NN-6(2): 332-348.[7]Szu H H, Telfer B, Kadambe B. Neural network adaptive wavelets for signal representation and classification[J].Optical Engineering.1992, 31(9):1907-1916[8]Waibel A, Hanazawa T, Hinton G, Shikano K, Lana K. Phone recognition using time-delay neural networks. IEEE Trans On ASSP, 1989, ASSP-37(3): 328-339.
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