Dr. S. Sivanandam is a senior professor at the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India. He has authored numerous books on computational intelligence, but his Introduction to Neural Networks Using MATLAB 6.0 (published by Tata McGraw-Hill) stands out for one reason: it assumes no prior AI knowledge.
The book was written in the early 2000s, when MATLAB 6.0 (also known as MATLAB R12) was the state-of-the-art in numerical computing. Unlike modern deep learning texts that focus on Python and TensorFlow, Sivanandam’s approach is algorithm-centric. He explains the neuron, the activation function, the learning rule, and then immediately shows the MATLAB code.
If you obtain a legitimate copy, you’ll notice that MATLAB 6.0 (circa 2001) uses slightly different syntax. Here’s how to update it:
| Old (MATLAB 6.0) | Modern Replacement |
|----------------|--------------------|
| newff (create feedforward net) | feedforwardnet |
| train (training function) | train (still works, but use trainNetwork for deep learning) |
| sim (simulate) | net(input) or predict |
| Hard-coded weight updates with loops | Use vectorized operations or automatic differentiation |
For example, a simple perceptron rule in modern MATLAB would leverage dot products rather than nested for loops—making it both faster and cleaner. (Note: Always ensure you access digital materials through
If you are looking for the digital version, it is widely cataloged in university libraries and academic repositories. You can often find it by searching specifically for the ISBN or using academic search engines:
(Note: Always ensure you access digital materials through legitimate library loans or open-access repositories to respect copyright laws.)
Introduction to Neural Networks Using MATLAB 6.0 by S.N. Sivanandam, S. Sumathi, and S.N. Deepa is a foundational textbook designed for undergraduate students in computer science and engineering. The primary feature of the book is its comprehensive integration of MATLAB
throughout the text, allowing readers to transition immediately from theoretical concepts to practical simulations SapnaOnline Key Content Features PSG College of Technology
The book provides a systematic overview of neural network architectures and learning algorithms, specifically focusing on: Fundamental Models
: Covers basic building blocks like the McCulloch-Pitts neuron model and core terminologies such as weights, bias, threshold, and activation functions. Classical Architectures
: Detailed explanations of Perceptron networks (single and multilayer), Adaline, and Madaline networks. Advanced Learning Models
: Includes sections on Associative Memory networks, Feedback networks, and Adaptive Resonance Theory (ART). Learning Rules the activation function
: Explores various training strategies, including Hebbian, Perceptron, Delta (Widrow-Hoff), Competitive, and Boltzmann learning rules. Practical and MATLAB-Specific Features Hands-on Implementation MATLAB 6.0 and the Neural Network Toolbox to solve numerous application examples. Vectorized Code
: The provided MATLAB scripts are optimized and vectorized to handle high-dimensional engineering problems efficiently. Real-World Applications
: Demonstrates how neural networks are applied in diverse fields such as
bioinformatics, robotics, healthcare, image processing, and communication Support Material
: Features summary sections, review questions at the end of each chapter, and supplemental MATLAB code files available for download to aid in research and exam preparation. For more information, you can view details on the MathWorks Book Page or help with a MATLAB code example from this book? Introduction To Neural Networks Using MATLAB | PDF - Scribd