A novel strategy of probability density function(PDF) shape control is proposed in stochastic systems. The controller is designed whose parameters are optimally obtained through the improved particle swarm optimization algorithm. The parameters of the controller are viewed as the space position of a particle in particle swarm optimization algorithm and updated continually until the controller makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The controller is excellent in making the state PDF follow the expected PDF and has the very small error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.
针对非线性Wiener模型的参数辨识问题,提出了一种基于Sigmoid函数及自适应算子改进差分进化(improved differential evolution algorithm with Sigmoid function and adaptive mutation operator,SADE)算法的参数辨识方法。利用Sigmoid函数及自适应变异算子改进了基本差分进化算法的变异操作部分,改进的方法能够有效地克服基本差分进化算法的过早收敛和不稳定性等缺点。将该改进差分进化算法应用于对非线性Wiener模型的参数辨识问题,达到了较高的辨识精度。在仿真试验中,与其它已有方法进行比较,仿真结果说明了所给的参数辨识方法是合理和有效的。
