The Research On Adaptive Control Of Fractional Order Stochastic Resonance And Its Application In Weak Signal Detection

Unlike the conventional belief that the noise signal will reduce the actual signal output quality,the stochastic resonance phenomenon indicates that the synergistic effect of noise and useful signals under certain conditions will improve the signal-to-noise ratio of the system output,and sometimes the noise can even be converted into a useful signal output,to enhance the output of the system.At present,the study of stochastic resonance is mainly focused on the integral calculus,and there are few researches on fractional stochastic resonance.However,for complex systems,the fractional order system has more anomalous properties,which makes the fractional calculus have the natural advantage over integer calculus.In the study of these few fractional stochastic resonances,the actual system parameters are mainly based on numerical simulations,which greatly limits the application value of fractional stochastic resonance.In this paper,we use the principle of single variable to analyze the internal mechanism of the fractional nonlinear bistable system,the influencing factors and the mechanism of each factor on the system.After weighing the complexity of the algorithm and the accuracy of the algorithm,we construct and propose a genetic algorithm to globally optimize the system.The algorithm avoids the whole system into the local optimum by selecting,crossing and mutation.Based on the signal-to-noise ratio(SNR),the three critical parameters of the fractional-order nonlinear bistable system are obtained by the adaptive control.The fractional order of the fractional-order nonlinear bistable system ?,the bistable system parameters b and The noise intensity D achieves the overall optimization of the whole system.The traditional Langevin equation is extended from the integer order to the fractional order,and the genetic algorithm is used to adjust the fractional order stochastic resonance correlation parameter.The results show that the algorithm proposed in this paper can extract the actual signal which has been submerged in the noise.The results show that the algorithm proposed in this paper can be used to extract the actual signal.