Three-layer neural networks for spectral estimation

A three-layer neural network is trained to perform spectral estimation. Excellent performance is found through computer simulations. When compared with an equivalent length radix-2 fast Fourier transform, the neural network has the following advantages: (1) the neural network can suppress the signal magnitude in sidelobes without widening the mainlobe; (2) the neural network completes its computation in two stages regardless of the number of input signal samples; (3) the number of input signal samples does not need to be an integer power of two; and (4) the neural network still generates valid results even if there are 25% broken interconnects uniformly distributed between the output layer and the hidden layer or between the hidden layer and the input layer. An application example of using a three-layer neural network for moving target detection is also included