Synthesis of a nonrecurrent associative memory model based on anonlinear transformation in the spectral domain

A new nonrecurrent associative memory model is proposed. This model is composed of a nonlinear transformation in the spectral domain followed by the association. The Moore-Penrose pseudoinverse is employed to obtain the least squares optimal solution. Computer simulations are done to evaluate the performance of the model. The simulations use one-dimensional speech signals and two-dimensional head/shoulder images. Comparison of the proposed model with the classical optimal linear associative memory and an optimal nonlinear associative memory is presented.