Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

DOI10.1080/00207720903045783zbMath1301.93099OpenAlexW2077829988MaRDI QIDQ2932630

Chi-Yung Lee, Cheng-Jian Lin

Publication date: 3 December 2014

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207720903045783

zbMATH Keywords

neuro-fuzzy controlparticle swarm optimisationnon-linear systems

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Nonlinear systems in control theory (93C10)

Related Items (9)

Profit optimisation of the multiple-vacation machine repair problem using particle swarm optimisation ⋮ A new encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem ⋮ An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink ⋮ A recurrent neural fuzzy controller based on self‐organizing improved particle swarm optimization for a magnetic levitation system ⋮ AN EFFICIENT EVOLUTIONARY NEURAL FUZZY CONTROLLER FOR THE INVERTED PENDULUM SYSTEM ⋮ Optimization analysis of the machine repair problem with multiple vacations and working breakdowns ⋮ Splines and polynomial tools for flatness-based constrained motion planning ⋮ Differential evolution and differential ant-stigmergy on dynamic optimisation problems ⋮ A fuzzy record-to-record travel algorithm for solving rough set attribute reduction

Cites Work

• Ten years of genetic fuzzy systems: Current framework and new trends.
• New evolutionary direction operator for genetic algorithms

This page was built for publication: Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation