Identification and control of nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network

DOI10.1016/J.NONRWA.2007.03.008zbMath1154.93375OpenAlexW2019091079MaRDI QIDQ1003205

Wen-Li Du, Lu Wang, Hong-Wei Ge, Feng Qian, Yan-Chun Liang

Publication date: 27 February 2009

Published in: Nonlinear Analysis. Real World Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nonrwa.2007.03.008

zbMATH Keywords

nonlinear system identificationparticle swarm optimizationultrasonic motorsystem controldynamic recurrent neural network

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Neural networks for/in biological studies, artificial life and related topics (92B20) System identification (93B30) Adaptive control/observation systems (93C40)

Related Items (6)

Fractional order neural networks for system identification ⋮ Hybrid neural network control of uncertain switched nonlinear systems with bounded disturbance ⋮ An improved adaptive tracking controller of permanent magnet synchronous motor ⋮ Robust intelligent backstepping control system using RCMAC for tracking periodic trajectories ⋮ Parameter identification in contact problems for Gao beam ⋮ MATLAB software for inversion of describing functions

Cites Work

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• High performance robust linear controller synthesis for an induction motor using a multi-objective hybrid control strategy
• The particle swarm optimization algorithm: Convergence analysis and parameter selection
• Active Vibration Control of a Modular Robot Combining a Back-Propagation Neural Network with a Genetic Algorithm
• Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity

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