Abstract
In this paper, hardware implementation of a wavelet neural network (WNN) is described. The WNN is developed in MATLAB and implemented on a Field-Programmable Gate Array (FPGA) device. The structure of the WNN is similar to the radial basis function (RBF) network, except that here the radial basis functions are replaced by orthonormal scaling functions. The training of the WNN is simplified due to the orthonormal properties of the scaling functions. The performances of the proposed WNN are tested by applying for the function approximation, system identification and the classification problems. Because of their parallel processing properties, the FPGAs provide good alternative in real-time applications of the WNN. By means of the simple scaling function used in the WNN architecture, it can be favorable to multilayer feedforward neural network and the RBF Networks implemented on the FPGA devices.
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© 2006 Springer-Verlag Berlin Heidelberg
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Karabıyık, A., Savran, A. (2006). Hardware Implementation of a Wavelet Neural Network Using FPGAs. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_121
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DOI: https://doi.org/10.1007/11893295_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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