Artificial Neural Networks and Type-2 Fuzzy Set

Artificial Neural Networks and Type-2 Fuzzy Set by Snehashish Chakraverty, Arup Kumar Sahoo, and Dhabaleswar Mohapatra is a definitive resource for engineers, data scientists, and advanced students tackling uncertainty in intelligent systems. This clear, contemporary guide bridges the gap between neural computation and Type-2 fuzzy logic, delivering practical frameworks for robust pattern recognition, control design, and decision-making under imprecision.

Discover concise explanations of core concepts—from multilayer perceptrons and learning algorithms to the theory and implementation of Type-2 fuzzy sets—paired with real-world examples and case studies relevant to robotics, signal processing, and industrial automation. The authors combine academic rigor with hands-on insight, making complex topics accessible without sacrificing technical depth.

Ideal for readers in India, North America, Europe and beyond, this book supports researchers and practitioners who need reliable methods to model uncertainty in modern machine learning and control applications. Whether you’re refining neural network architectures, exploring fuzzy logic integration, or developing resilient AI systems for real-world environments, you’ll find actionable strategies and tested approaches here.

Gain greater confidence in designing systems that tolerate noisy data and ambiguous inputs. Enhanced clarity, practical guidance, and methodical presentation make this title a must-have reference on your shelf or in your lab.

Order your copy today and empower your projects with advanced techniques in neural networks and Type-2 fuzzy logic—relevant for academic courses, industrial research, and applied AI development worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.