BOOKS - Data-Driven Modelling with Fuzzy Sets Embracing Uncertainty
Data-Driven Modelling with Fuzzy Sets Embracing Uncertainty - Said Broumi, D. Nagarajan, Michael Gr. Voskoglou, S.A. Edalatpanah 2024 PDF CRC Press BOOKS
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Data-Driven Modelling with Fuzzy Sets Embracing Uncertainty
Author: Said Broumi, D. Nagarajan, Michael Gr. Voskoglou, S.A. Edalatpanah
Year: 2024
Format: PDF
File size: 10.1 MB
Language: ENG



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A. K. Bhatia. The book "Data-Driven Modeling with Fuzzy Sets Embracing Uncertainty" by Dr. A. K. Bhatia is a groundbreaking work that explores the intersection of data science, fuzzy logic, and uncertainty analysis. The author presents a comprehensive framework for modeling complex systems using fuzzy sets and probability theory, providing readers with a powerful toolkit for tackling real-world problems. This book is essential reading for anyone interested in understanding how data-driven modeling can be used to embrace uncertainty and drive decision-making in a rapidly changing world. The book begins by introducing the concept of fuzzy sets, which are sets with imprecise or ambiguous boundaries. These sets are ubiquitous in real-world data, where exact values are often difficult to obtain. The author demonstrates how fuzzy sets can be combined with probability theory to create robust models that capture the inherent uncertainty of complex systems.
А. К. Бхатия. Книга «Data-Driven Modeling with Fuzzy Sets Ambracing Uncertainty» доктора А. К. Бхатия является новаторской работой, которая исследует пересечение науки о данных, нечеткой логики и анализа неопределенностей. Автор представляет комплексную основу для моделирования сложных систем с использованием нечетких множеств и теории вероятностей, предоставляя читателям мощный инструментарий для решения реальных проблем. Эта книга является важным чтением для всех, кто заинтересован в понимании того, как моделирование на основе данных может использоваться для принятия неопределенности и принятия решений в быстро меняющемся мире. Книга начинается с введения понятия нечётких множеств, представляющих собой множества с неточными или неоднозначными границами. Эти наборы повсеместно распространены в реальных данных, где точные значения часто трудно получить. Автор демонстрирует, как нечёткие множества могут сочетаться с теорией вероятностей для создания надёжных моделей, улавливающих присущую сложным системам неопределённость.
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