Learning of fast transforms and spectral domain neural computing

The interaction between neural networks and fast transforms is examined. It is shown that the development, discovery, and the study of transforms can be efficiently carried out through the use of learning algorithms used in neural networks. In turn, these transforms can be used for a number of tasks in neural networks, such as network reduction and simplification, fast convergence during learning, fast memory retrieval, reduced cost and increased speed of implementation, feature extraction, invariance to distortions, better generalization, and increased quality of performance in the presence of noise and incomplete knowledge. Learning with the unconstrained part of the neural network of reduced size or minimized number of interconnections is performed in the spectral domain only, thereby considerably easing the problems of convergence and implementation. The techniques described can be especially useful in dynamic neural networks