Application of Particle Swarm Optimization in System Identification

In the field of system identification, Particle Swarm Optimization (PSO) is widely applied and effectively identifies system parameters. PSO is an optimization algorithm inspired by social behavior patterns, where a population of candidate solutions (particles) moves through the search space according to their velocity and position updates based on individual and collective best experiences. The algorithm evaluates particles using fitness functions that typically measure the error between model outputs and actual system responses. Implementation involves key parameters like inertia weight, cognitive and social coefficients, which balance exploration and exploitation during the optimization process. The application of PSO in system identification has been validated across multiple domains including control systems and power systems. Through PSO, both the accuracy and efficiency of system identification have been significantly improved, providing robust support for practical engineering applications. The algorithm can be implemented using MATLAB's optimization toolbox or custom code with functions handling particle initialization, velocity updates, and fitness evaluation.