Discrete Walsh transform processor based on Hopfield neural network

The discrete Walsh transform (DWT) is one of the most important techniques as well as the discrete Fourier transform (DFT) in the field of signal processing. We have proposed the way how to construct the DWT processor based on Hopfield linear programming neural networks. In this paper, we describe the convergence of DWT processor based on Hopfield neural networks. First, the influence of the orthonormal matrix on solving linear equations by steepest descent (SD) method is investigated and this theory is applied to the convergence of the DWT processor composed of Hopfield neural networks. Finally it is shown both analytically and by simulation that this type of neural networks is suitable for orthogonal transform such as DWT