Parameter Adjustment of Nonlinear Controllers via a Revised Genetic Algorithm

Nonlinear parameter estimation via the genetic algorithm

A modified genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The scheme is also applied to feedforward and recurrent neural networks.

Genetic Algorithm in Parameter Estimation of Nonlinear Dynamic Systems

Parameter estimation for nonlinear dynamical adjustment models

A recursive generalized least squares algorithm and a filtering based least squares algorithm are developed for input nonlinear dynamical adjustment models with memoryless nonlinear blocks followed by linear dynamical blocks. The basic idea is to use the filtering technique and to replace the unknown terms in the information vectors with their estimates. The simulation results show the performa...

A parameter-less genetic algorithm

From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press...

Pareto Design of Decoupled Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm

This paper presents Pareto design of decoupled sliding-mode controllers based on amultiobjective genetic algorithm for several fourth-order coupled nonlinear systems. In order to achieve an optimum controller, at first, the decoupled sliding mode controller is applied to stablize the fourth-order coupled nonlinear systems at the equilibrium point. Then, the multiobjective genetic algorithm is a...