A novel online performance evaluation strategy to analog circuit

An analog circuit performance online evaluation approach is presented subject to the inevitable actualities of the fault value caused during the data collection process. The multi-model with the corresponding features is modeled via fuzzy clustering based data features firstly. And then the developed scheme relies on a weighted combination of normal least square support vector regression (LSSVR) and particle swarm optimization (PSO) to realize the active suppression for the wrong value and disturbance parameters. Furthermore, another problem should be considered; namely, the traditional offline evaluation approach could not realize the model׳s timely adjustment with the sample increasing or decreasing. Focusing on this issue, the increase and decrease interaction update idea is imported to the modified performance evaluation scheme. The developed model can be updated quickly online. Numerical testing data information supported by the college analog circuit experiments adopted eight performance indexes of the traditional OTL amplifier to establish training set. This data information had been obtained via precision instrument evaluation in two years. Numerical simulations are preformed to verify the performance of the proposed approach.