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Principles of Soft Computing by S.N.Sivanandam and S.N.Deepa: A Review
Soft computing is a branch of artificial intelligence that deals with approximate reasoning, uncertainty, imprecision, and learning. It encompasses techniques such as neural networks, fuzzy logic, genetic algorithms, and swarm intelligence. These techniques can be used to solve complex problems that are difficult or impossible to solve by conventional methods.
Principles of Soft Computing by S.N.Sivanandam and S.N.Deepa is a comprehensive textbook that covers the basic concepts and applications of soft computing. The book is divided into four parts: Part I introduces the fundamentals of soft computing, Part II discusses neural networks and their architectures, Part III explains fuzzy logic and its applications, and Part IV explores genetic algorithms and their variants. The book also includes a CD-ROM that contains MATLAB programs for implementing various soft computing techniques.
The book is written in a clear and simple language, with numerous examples, diagrams, tables, and exercises. The book is suitable for undergraduate and postgraduate students of computer science, engineering, management, and operational research. It can also be useful for researchers and practitioners who want to learn the basics of soft computing and its applications.
The book is available in various formats such as paperback[^1^], PDF[^2^] [^3^], and online. However, some users have reported that some pages are not available or readable in some formats. Therefore, it is advisable to check the quality and availability of the format before downloading or purchasing the book.Some of the topics covered in the book are:
Soft computing vs. hard computing: The book explains the differences and similarities between soft computing and hard computing, and the advantages and limitations of each approach.
Neural network models: The book describes various neural network models such as feedforward networks, recurrent networks, radial basis function networks, self-organizing maps, learning vector quantization, and adaptive resonance theory. It also discusses the learning algorithms, activation functions, and applications of neural networks.
Fuzzy logic concepts: The book introduces the basic concepts of fuzzy logic such as fuzzy sets, fuzzy relations, fuzzy operators, fuzzy rules, and fuzzy inference. It also explains the applications of fuzzy logic in areas such as control systems, pattern recognition, decision making, and optimization.
Genetic algorithm operators: The book explains the basic operators of genetic algorithms such as selection, crossover, mutation, and elitism. It also describes the variants of genetic algorithms such as real-coded genetic algorithms, binary-coded genetic algorithms, micro-genetic algorithms, and multi-objective genetic algorithms. It also illustrates the applications of genetic algorithms in areas such as scheduling, clustering, classification, and feature selection.
The book is well-organized and easy to follow. It provides a good balance between theory and practice. It also includes a glossary of terms and a list of references for further reading. The book is a valuable resource for anyone who wants to learn the principles of soft computing and its applications. aa16f39245