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Introduction To Neural Networks Using MATLAB 60 By S N Sivanandam Sumathi Amp Deepahottorrent: A Comprehensive Guide


Introduction To Neural Networks Using MATLAB 60 By S N Sivanandam Sumathi Amp Deepahottorrent: A Comprehensive Guide




Neural networks are powerful computational models that can learn from data and perform various tasks such as classification, regression, clustering, and optimization. MATLAB is a popular programming language and environment that offers many tools and functions for working with neural networks. In this article, we will review the book "Introduction To Neural Networks Using MATLAB 60 By S N Sivanandam Sumathi Amp Deepahottorrent", which is a comprehensive guide for beginners and experts alike who want to learn more about neural networks and how to implement them using MATLAB.


The book covers the basic concepts and principles of neural networks, such as artificial neurons, activation functions, learning rules, network architectures, and applications. It also introduces some advanced topics such as fuzzy neural networks, genetic algorithms, swarm intelligence, and hybrid systems. The book provides many examples and exercises that illustrate the theory and practice of neural networks using MATLAB. The book also comes with a CD-ROM that contains the source code and data sets for all the examples and exercises in the book.


Introduction To Neural Networks Using MATLAB 60 By S N Sivanandam Sumathi Amp Deepahottorrent


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The book is divided into 16 chapters, each focusing on a different aspect of neural networks and MATLAB. The chapters are as follows:


  • Chapter 1: Introduction to Neural Networks



  • Chapter 2: Fundamentals of Artificial Neural Networks



  • Chapter 3: Supervised Learning Neural Networks



  • Chapter 4: Radial Basis Function Networks



  • Chapter 5: Unsupervised Learning Neural Networks



  • Chapter 6: Associative Memory Networks



  • Chapter 7: Adaptive Resonance Theory Networks



  • Chapter 8: Competitive Learning Networks



  • Chapter 9: Self-Organizing Maps



  • Chapter 10: Fuzzy Logic and Fuzzy Neural Networks



  • Chapter 11: Genetic Algorithms and Genetic Neural Networks



  • Chapter 12: Particle Swarm Optimization and Swarm Intelligence