Intuitionistic Fuzzy Ann Using Linear Space Techniques & Python - Tapa blanda

Robinson P, John ; A, Saranraj ; S A, Arun Raja

9786209749650: Intuitionistic Fuzzy Ann Using Linear Space Techniques & Python

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ISBN 10: 6209749658 ISBN 13: 9786209749650

Editorial: LAP Lambert Academic Publishing, 2026

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Intuitionistic Fuzzy ANN Using Linear Space Techniques & Python presents a unified framework that integrates intuitionistic fuzzy set (IFS) theory with artificial neural networks (ANN) using linear space methodologies and computational tools in Python. The book addresses decision-making and learning problems involving uncertainty, hesitation, and incomplete information by embedding IFS-based representations, membership, non-membership, and hesitation into neural learning models. It systematically develops the mathematical foundations of IFS, linear algebraic learning structures, and ANN paradigms including perceptron, delta rule, and backpropagation. The proposed approach interprets learning geometrically through vector space concepts such as norms, projections, and transformations. The book also explores aggregation operators for MAGDM, hybrid fuzzy-neural architectures, and defuzzification-based learning strategies. Python implementations, algorithms, and case studies demonstrate applicability across engineering, environmental, healthcare, and policy decision systems, ensuring accuracy, stability, and reproducibility.

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Editorial: LAP Lambert Academic Publishing
A�o de publicaci�n: 2026
Idioma: Ingl�s
ISBN 10: 6209749658
ISBN 13: 9786209749650
Encuadernaci�n: Tapa blanda
N�mero de p�ginas: 160
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Intuitionistic Fuzzy Ann Using Linear Space Techniques and Python

John Robinson P

Publicado por LAP LAMBERT Academic Publishing, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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INTUITIONISTIC FUZZY ANN USING LINEAR SPACE TECHNIQUES & PYTHON

John Robinson P

Publicado por LAP LAMBERT Academic Publishing M�r 2026, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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Intuitionistic Fuzzy Ann Using Linear Space Techniques & Python (Paperback)

John Robinson P.

Publicado por LAP Lambert Academic Publishing, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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Paperback. Condici�n: new. Paperback. Intuitionistic Fuzzy ANN Using Linear Space Techniques & Python presents a unified framework that integrates intuitionistic fuzzy set (IFS) theory with artificial neural networks (ANN) using linear space methodologies and computational tools in Python. The book addresses decision-making and learning problems involving uncertainty, hesitation, and incomplete information by embedding IFS-based representations, membership, non-membership, and hesitation into neural learning models. It systematically develops the mathematical foundations of IFS, linear algebraic learning structures, and ANN paradigms including perceptron, delta rule, and backpropagation. The proposed approach interprets learning geometrically through vector space concepts such as norms, projections, and transformations. The book also explores aggregation operators for MAGDM, hybrid fuzzy-neural architectures, and defuzzification-based learning strategies. Python implementations, algorithms, and case studies demonstrate applicability across engineering, environmental, healthcare, and policy decision systems, ensuring accuracy, stability, and reproducibility. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N� de ref. del art�culo: 9786209749650

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INTUITIONISTIC FUZZY ANN USING LINEAR SPACE TECHNIQUES & PYTHON

John Robinson P (u. a.)

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Taschenbuch. Condici�n: Neu. INTUITIONISTIC FUZZY ANN USING LINEAR SPACE TECHNIQUES & PYTHON | John Robinson P (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209749650 | Verantwortliche Person f�r die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N� de ref. del art�culo: 134948827

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INTUITIONISTIC FUZZY ANN USING LINEAR SPACE TECHNIQUES & PYTHON

John Robinson P

Publicado por LAP LAMBERT Academic Publishing M�r 2026, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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Taschenbuch. Condici�n: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Intuitionistic Fuzzy ANN Using Linear Space Techniques & Python presents a unified framework that integrates intuitionistic fuzzy set (IFS) theory with artificial neural networks (ANN) using linear space methodologies and computational tools in Python. The book addresses decision-making and learning problems involving uncertainty, hesitation, and incomplete information by embedding IFS-based representations, membership, non-membership, and hesitation into neural learning models. It systematically develops the mathematical foundations of IFS, linear algebraic learning structures, and ANN paradigms including perceptron, delta rule, and backpropagation. The proposed approach interprets learning geometrically through vector space concepts such as norms, projections, and transformations. The book also explores aggregation operators for MAGDM, hybrid fuzzy-neural architectures, and defuzzification-based learning strategies. Python implementations, algorithms, and case studies demonstrate applicability across engineering, environmental, healthcare, and policy decision systems, ensuring accuracy, stability, and reproducibility.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 160 pp. Englisch. N� de ref. del art�culo: 9786209749650

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INTUITIONISTIC FUZZY ANN USING LINEAR SPACE TECHNIQUES & PYTHON

John Robinson P

Publicado por LAP LAMBERT Academic Publishing, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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Intuitionistic Fuzzy Ann Using Linear Space Techniques & Python (Paperback)

John Robinson P.

Publicado por LAP Lambert Academic Publishing, 2026

ISBN 10: 6209749658 ISBN 13: 9786209749650

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Paperback. Condici�n: new. Paperback. Intuitionistic Fuzzy ANN Using Linear Space Techniques & Python presents a unified framework that integrates intuitionistic fuzzy set (IFS) theory with artificial neural networks (ANN) using linear space methodologies and computational tools in Python. The book addresses decision-making and learning problems involving uncertainty, hesitation, and incomplete information by embedding IFS-based representations, membership, non-membership, and hesitation into neural learning models. It systematically develops the mathematical foundations of IFS, linear algebraic learning structures, and ANN paradigms including perceptron, delta rule, and backpropagation. The proposed approach interprets learning geometrically through vector space concepts such as norms, projections, and transformations. The book also explores aggregation operators for MAGDM, hybrid fuzzy-neural architectures, and defuzzification-based learning strategies. Python implementations, algorithms, and case studies demonstrate applicability across engineering, environmental, healthcare, and policy decision systems, ensuring accuracy, stability, and reproducibility. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de ref. del art�culo: 9786209749650

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