Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems

Chapter 6 presents the application of bio-inspired algorithms to improve neural identifiers for discrete-time unknown nonlinear systems. PSO is particularly used to improve two kinds of neural identifiers: first, PSO is used to find initial conditions of an EKF learning algorithm (enhanced PSO-EKF) to train a RHONN in order to identify a dynamic mathematical model of a linear induction motor benchmark; second, the enhanced PSO-EKF is used to train a recurrent multilayer perceptron in order to obtain an accurate neural model for forecasting in smart grids. Importance of these applications is attributable to the need of accurate dynamic models for modern purposes, such as control, forecasting, simulation, and emulation. In addition to the foregoing applications the PSO-EKF combination has shown its applicability for the proposed schemes for different kinds of unknown nonlinear-systems with noises, uncertainties, delays, saturations, etc.

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