: Adjustable parameters that are modified during the learning process to minimize error.
: The authors apply these techniques to diverse fields, including bioinformatics, robotics, healthcare, and image processing. Why This Specific Text is Sought After : Adjustable parameters that are modified during the
Sivanandam et al. provide detailed algorithmic explanations for several foundational learning rules: It establishes that an Artificial Neural Network (ANN)
: The book covers various structures, ranging from simple Single-Layer Perceptrons to more complex Multilayer Feedforward Networks and Feedback Networks . Key Learning Rules Covered : Adjustable parameters that are modified during the
: Focused on minimizing the Least Mean Square (LMS) error.
The book begins by comparing the human brain's biological neural networks with artificial models. It establishes that an Artificial Neural Network (ANN) is an adaptive system that learns through interconnected nodes (neurons), which are characterized by:
by S.N. Sivanandam, S. Sumathi, and S.N. Deepa is a fundamental resource for students and engineers seeking to bridge the gap between biological intelligence and computational models. Originally published by Tata McGraw-Hill, this text has become a staple for introductory courses due to its practical integration of MATLAB examples throughout the theoretical discussions. Core Concepts and Theoretical Foundations