Compensatory neural fuzzy network with symbiotic particle swarm optimization for temperature control

DOI10.1016/J.APM.2014.05.040zbMath1449.93165OpenAlexW2035710013MaRDI QIDQ1634998

Cheng-Hung Chen, Chun-Cheng Peng

Publication date: 18 December 2018

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2014.05.040

zbMATH Keywords

particle swarm optimizationneural fuzzy networkssymbiotic evolutionwater bath temperature system

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy control/observation systems (93C42) Adaptive control/observation systems (93C40) Networked control (93B70)

Related Items (2)

Design of unsupervised fractional neural network model optimized with interior point algorithm for solving Bagley-Torvik equation ⋮ Optimizing high-dimensional functions with an efficient particle swarm optimization algorithm

Cites Work

• A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
• Identification and prediction using recurrent compensatory neuro-fuzzy systems
• Evolving rule-based models. A tool for design of flexible adaptive systems.
• Genetic Fuzzy Systems
• Global adaptive output-feedback control of nonlinear systems. I. Linear parameterization
• Global adaptive output-feedback control of nonlinear systems. II. Nonlinear parameterization
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