Modelling of the automatic depth control electrohydraulic system using RBF neural network and genetic algorithm

DOI10.1155/2010/124014zbMath1202.93097OpenAlexW2169133431WikidataQ58652909 ScholiaQ58652909MaRDI QIDQ613799

Zong-Yi Xing, Yuan Zhang, Yong Qin, Xue-Miao Pang, Limin Jia

Publication date: 23 December 2010

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://eudml.org/doc/227732

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Application models in control theory (93C95)

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Radial basis functional link network and Hamilton Jacobi Issacs for force/position control in robotic manipulation

Cites Work

• Modelling public transport trips by radial basis function neural networks
• Radial Basis Functions
• Kernel Based Learning Methods: Regularization Networks and RBF Networks
• Radial basis function networks 2. New advances in design
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