Fuzzy Machine Learning: Advanced Approaches to Solve Optimization Problems 9783110603880

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Автор: Arindam Chaudhuri
Название: Fuzzy Machine Learning: Advanced Approaches to Solve Optimization Problems
ISBN: 9783110603880
Издательство: Walter de Gruyter
Классификация: ISBN-10: 3110603888
Обложка/Формат: Ebook
Страницы: 150
Вес: 0.00 кг.
Дата издания: 01.01.2021
Серия: Computing & IT
Язык: English
Читательская аудитория: Professional and scholarly
Ключевые слова: Artificial intelligence,Automatic control engineering,Computer science,Electrical engineering, COMPUTERS / Information Theory,COMPUTERS / Intelligence (AI) & Semantics,MATHEMATICS / Optimization,TECHNOLOGY & ENGINEERING / Automation
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Поставляется из: Германии
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The continuous increase in demand towards lowering production costs to withstand cutthroat competition has prompted researchers to look for rigorous methods of decision making. As search for best has always fascinated mankind, operations or strategies have been attempted and devised for searching optimum solutions for variety of problems in all branches of activities perceived by logic or intitution or both. With this motivation in this work novel fuzzy machine learning algorithms are proposed to develop optimum solutions for several optimization problems. The emphasis of proposed methodologies is given on handling data sets which are large both in size and dimension and involves classes that are overlapping, intractable and have non-linear boundaries. Several strategies based on data reduction, dimensionality reduction, active learning efficient search heuristics are employed for dealing with scaling issues The problems handle linguistic input and ambiguous output decision, learning of overlapping and intractable class structures, selection of optimal parameters and discovering human comprehensible knowledge in form of linguistic rules. The different features of methodologies along with comparisons with those of related ones are demonstrated extensively on different real life data sets. The experimental data have been considered from variety of domains. The superiority of models over the benchmark are found to be effective and significant.

This book is generally suited for bachelor, master and research students working for courses in engineering optimization, operations research and operations management. It will also be immensely beneficial to industry professions as well as to scientists and postdocs working at research institutes working in abovementioned domains. The prima face of this book is to highlight certain insights and findings for some important optimization problems over the period of past 10 years. All the proposed algorithms are of continuous evolving nature. The problems considered here include traveling salesman problem, transportation problem, non-convex optimization, dynamic programming, strategic games, resource allocation problem and job assignment problems. The solutions of these problems are developed using machine learning approaches by integrating fuzzy logic, neural networks, deep learning, and genetic algorithms.

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