Arbitrary FIR filter synthesis using a neural network

We propose a neural network to synthesize an arbitrary FIR filter in a least square sense. The network can evolve to its steady-states or equilibrium points from any initial state in the magnitude of the circuit's time constant. Under the steady-state, the output of the network is just our designed FIR filter coefficient if a real, symmetric, and positive-definite matrix calculated by the design specifications is directly used as the synaptic strength matrix.