Parameter identification and dynamic response analysis of a modified Prandtl–Ishlinskii asymmetric hysteresis model via least-mean square algorithm and particle swarm optimization

Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of numerous structural systems under high intensity cyclic or random loading, as well as in numerous mechanical and electromagnetic systems. For several decades, hysteretic response analysis of structural systems has been widely studied and numerous hysteresis models have been proposed and utilized in order to reproduce and better understand the complex hysteretically degrading behavior of structural systems. An important area of research in this regard has been the parameter identification of hysteretic systems. In this paper, we propose a modified Prandtl–Ishlinskii model to simulate the asymmetric hysteresis, which is the complex behavior in structural systems. In addition, a new approach based on particle swarm optimization and least-mean square algorithm is utilized for parameter identification of this hysteresis model. Finally, the model is applied in structural dynamic response analysis of a base isolated structural model under seismic load.

Improved Adaptive Beamforming for Smart Antennas

In this paper, we propose a new antenna array configuration for smart antenna beamforming. In this new method, we displace two antenna elements of a uniform linear array (ULA) and place them at the top and bottom of the array axis. We investigate the efficacy of this method by deploying the variable step size least mean square algorithm (VSSLMS). The proposed method is compared with popular LMS and normalized LMS algorithms. Computer simulations show that the proposed method has enhanced convergence rate and high data transmission compared to the LMS and the NLMS methods. Also, the new method has the same performance for middle angles, near boresight and array endfires which is not possible for the LMS and the NLMS method using a ULA.

A novel variable step size least mean square method for adaptive micro-vibration control

The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor.