A Neural Minor Component Analysis Algorithm to Beamforming

A minor component analysis(MCA) learning rule is presented which includes a penalty term on the self-stabilizing MCA learning rule.After a presentation of steady-state and convergence analysis,the learning rule is used for adaptive beamforming.Constrained beamformer power optimization principle is employed,which allows to improve the robustness of the beamforming algorithm by emphasizing white noise sensitivity control and prior knowledge about the disturbances.Computer simulations show the novel MCA learning rule has strong stability,interference resistance ability and real-time signal tracking ability,compared with the first minor component analysis(FMCA) learning rule which is used for robust constrained beamforming.