Fast learning neural networks using transform domain LMS structure

Transform domain adaptive filtering, which uses orthogonal transforms to partially uncorrelate the colored input, thus reducing the eigenvalue spread, is extended to the nonlinear domain and used as the basis of a learning algorithm for neural networks. Computer simulations show that this neural structure learns much faster than least-mean-square-based structure.<>