Deconvolution and nonlinear inverse filtering using a neural network

The authors describe a cerebellar model arithmetic computer (CMAC) neural network and its use in learning the inverse function necessary for deconvolution and nonlinear inverse filtering. Simulations are described that use random noise, telegraph, or bit string signals as inputs to linear and nonlinear systems to generate the signal to be inverse-filtered. Results are shown for linear systems with decaying sinusoidal impulse responses and nonlinear systems with memory having saturating nonlinearities. Examples with low noise and testing (nontraining) results with new random sequences are shown. The results show considerable promise