Blind Signal Noise Separation on Instant Mixing Nonlinear Circuits Based on MISEP Algorithm

This paper proposes a mutual information based blind signal noise separation algorithm of nonlinear circuit, to compensate the measured circuit signals. For the instant mixing non-linear circuits, this method constructs the multilayer perceptron network by feedback cascading the signal separation block and the parameter adjustment block; aiming at the minimum mutual information, it trains the network using the measured circuit signals with random noises, until the cost function value converges to pre-set error range; then the trained network is applied to the blind signal noise separation for the nonlinear circuits. The experimental results on the fore-nonlinear circuit, the post-nonlinear circuit and the single-stage amplifier circuit show that the signals and noises separated from this method approximately follows the circuit inputs on the time domain waveforms and power spectrum characteristics.