A neural implementation of robust broadband adaptive array

The computational complexity of robust adaptive array with quadratic constraints is a critical problem in real time implementation. For coping with this problem, the Chua's (1988) nonlinear programming recurrent neural network is explored, which is used to solve the optimal solution of the robust adaptive array with quadratic constraints. The present approach converges within several times of the circuit time constant, thus particularly suitable to real time applications.