Matrix representation of a binary relation using fuzzy and artificial learning theory. An algorithm which uses the potential functions learning rule

DOI10.1080/02331934.2013.864455zbMath1329.91119OpenAlexW1974486727MaRDI QIDQ2926499

Adela Socol, Leonardo Badea, Alina Constantinescu

Publication date: 24 October 2014

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2013.864455

zbMATH Keywords

choice functiondecision-makingartificial learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Individual preferences (91B08) Measurement and performance in psychology (91E45)

Related Items (1)

Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region

Cites Work

• Rationalizability of choice functions on general domains without full transitivity
• Sum-fuzzy implementation of a choice function using artificial learning procedure with fixed fraction.
• On choosing rationally when preferences are fuzzy
• Culture and the explanation of choice behavior
• A new approach to sum-fuzzy rational choice functions
• Procedural choice
• A multicriteria DSS for stock evaluation using fundamental analysis
• An integrated approach for stock evaluation and portfolio optimization
• Introduction to Financial Forecasting in Investment Analysis
• Portfolio construction on the Athens Stock Exchange: a multiobjective optimization approach

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