Convergence analysis of sliding mode trajectories in multi-objective neural networks learning

DOI10.1016/j.neunet.2012.04.006zbMath1258.68113OpenAlexW2068453882WikidataQ46231955 ScholiaQ46231955MaRDI QIDQ1941589

Benjamin Rodrigues de Menezes, Marcelo Azevedo Costa, Antônio Pádua Braga

Publication date: 13 March 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.04.006

zbMATH Keywords

neural networkssliding modemulti-objective learning

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Cites Work

• Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
• Estimating the dimension of a model
• A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems
• Improving generalization of MLPs with multi-objective optimization
• Support-vector networks
• Multi-objective machine learning
• On the stability of discrete-time sliding mode control systems
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Feature Article—The Ellipsoid Method: A Survey
• Learning representations by back-propagating errors
• On a Bicriterion Formulation of the Problems of Integrated System Identification and System Optimization
• Neural network adaptive robust control of nonlinear systems in semi-strict feedback form
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Convergence analysis of sliding mode trajectories in multi-objective neural networks learning