An architecture of neural networks with interval weights and its application to fuzzy regression analysis

DOI10.1016/0165-0114(93)90118-2zbMath0790.62072OpenAlexW2071974562MaRDI QIDQ1313045

Hideo Tanaka, Hidehiko Okada, Hisao Ishibuchi

Publication date: 28 June 1994

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0165-0114(93)90118-2

zbMATH Keywords

learning algorithmfuzzy regressioncost functionfuzzy modelsinclusion relationback propagation algorithminterval weightsarchitecture of neural networkscrisp datainterval biasesinterval modelsinterval outputinterval target

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Linear inference, regression (62J99) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (17)

Fuzzy nonparametric regression based on local linear smoothing technique ⋮ A parametric recurrent neural network scheme for solving a class of fuzzy regression models with some real-world applications ⋮ Constructive Approximation of Continuous Interval-Valued Functions ⋮ A fuzzy varying coefficient model and its estimation ⋮ Interval regression by tolerance analysis approach ⋮ Support vector interval regression networks for interval regression analysis. ⋮ Nonparametric fuzzy regression -- \(k\)-NN and kernel smoothing techniques. ⋮ ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS ⋮ Extended support vector interval regression networks for interval input-output data ⋮ On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data ⋮ Support vector interval regression machine for crisp input and output data ⋮ Fuzzy nonlinear regression analysis using a random weight network ⋮ A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution ⋮ Fuzzy regression by fuzzy number neural networks ⋮ Interval regression analysis using support vector networks ⋮ Unnamed Item ⋮ Pattern characteristics of an evolution between two classes

Cites Work

• Unnamed Item
• Fuzzy data analysis by possibilistic linear models
• The concept of a linguistic variable and its application to approximate reasoning. III
• Learning representations by back-propagating errors

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