The paper presents method of the modal parameters identification based on the Particle Swarm Optimization (PSO) algorithm [1]. The basic PSO algorithm is modified in order to achieve fast convergence and low estimation error of identified parameters values. The procedure of identification as well as algorithm modifications are presented and some simple examples for the SISO systems are provided. Results are compared with the results...

The paper presents the theoretical background and four applications examples of the new method for the estimation of support stiffness coefficients of complex structures modelled discretely (e.g. with the use of the Finite Element Model (FEM) method based on the modified Particle Swarm Optimization (PSO) algorithm. In real-life cases, exact values of the supports’ stiffness coefficients may change for various reasons...

The measurement of birefringence is useful for the examination of both technical and biological objects. One of the main problems is that the polarization state of light in birefringent media changes periodically. Without the knowledge of the period number, the birefringence of a given medium cannot be determined reliably. We propose to analyse the spectrum of light in order to determine the birefringence. We use a Particle Swarm...

The paper presents the new method of the natural frequencies and damping identification based on the Artificial Intelligence (AI) Particle Swarm Optimization (PSO) algorithm. The identification is performed in the frequency domain. The algorithm performs two PSO-based steps and introduces some modifications in order to achieve quick convergence and low estimation error of the identified parameters’ values for multi-mode systems....