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Hardware Implementation of a Wavelet Neural Network Using FPGAs

  • Ali Karabıyık
  • Aydoğan Savran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

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.

Keywords

Hide Layer Radial Basis Function Hardware Implementation Scaling Function Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ali Karabıyık
    • 1
  • Aydoğan Savran
    • 1
  1. 1.Department of Electrical and Electronics EngineeringEge UniversityİzmirTurkey

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