B.Sc. Computer Technology - Anna University

YCT017 NEURAL NETWORKS L T P C
3 0 0 3
UNIT – I INTRODUCTION TO NEURAL NETWORKS (9)
Differences between Biological and Artificial Neural networks – Typical Architecture, Common
Activation Functions, McCulloch – Pitts Neuron, Simple Neural Nets for Pattern Classification,
Linear Separability – Hebb Net, Perceptron, Adaline, Madaline – Architecture, Algorithm and
Simple Applications.
UNIT – II PATTERN ASSOCIATION (9)
Training Algorithms for pattern association – Heb rule and Delta rule, Heteroassociatiive,
autoassociative and iterative autoassociative Net, Bidirectional Associative Memory –
Architecture, Algorithm and Simple Applications.
UNIT – III NEURAL NETWORKS BASED ON COMPETITION (9)
Kohonen Self Organizing Maps, Learning Vector Quantization, Counter Propagation –
Architecture, Algorithm and Applications.
UNIT – IV ADAPTIVE RESONANCE AND BACKPROPAGATION NEURAL NETOWRKS (9)
ART1 and ART2 – Basic Operation and Algorithm, Standard Back propagation Architecture,
derivation of Learning Rules, Boltzmann Machine Learning – Architecture, Algorithm and Simple
Applications.
UNIT – V APPLICATIONS OF NEURAL NETWORKS (9)
Applications of Neural Networks: Pattern Recognition – Image Compression – Communication –
Control Systems.
Total: 45
REFERENCES
1. Sivandam S N, Sumathi S, Deepa S N, “Introduction to Neural Networks using Matlab 6.0”,
Tata McGrawHill Publications, New Delhi, 2005.
2. Laurene Faysett, “Fundamentals of Neural Networks”, Pearson Education India, New Delhi,
2004.
3. Limin Fu, “Neural Networks in Computer Intelligence”, Tata McGrawHill Publications, New
Delhi, 2006.
YCS017 FUZZY LOGIC L T P C
3 0 0 3
UNIT – I 9
Introduction – Background – Uncertainty and Imprecision – Statistics and Random Processes –
Uncertainly in Information – Fuzzy Sets and Membership – Chance versus Ambiguity –
Classical Sets and Fuzzy Sets – Classical Sets – Fuzzy Sets – Sets as Points in Hypercubes.
UNIT – II 9
Classical Relations and Fuzzy Relations – Cartesian product – Crisp Relations – Fuzzy
Relations – Tolerance and Equivalence Relations – Fuzzy Tolerance and Equivalence Relations
– Value Assignments.
44