Fuzzy set theory: Fuzzy versus crisp- Crisp sets: Operations on crisp sets – Properties of crisp sets – Partition and covering .Fuzzy sets: Membership function basic fuzzy set operations – Properties of fuzzy sets.
Crisp relations Cartesian product – Other crisp relations –Operations on relations. Fuzzy relations: Fuzzy Cartesian product – Operations on fuzzy relations. Fuzzy systems: Crisp Logic: Laws of prepositional Logic- Inference in Prepositional Logic. Predicate Logic: Interpretations of Predicate Logic formula – Inference in predicate Logic
Fundamentals of Neural Network: Basic Concepts of Neural Networks Human Brain – Model of an Artificial Neuron – Neural Network Architectures: Single Layer Feed Forward Network – Multilayer Feed forward Network
Early neural architecture: Rosenblatt’s Perceptron-ADALINE network-MADALINE network-Some application domains. Back Propagation Networks: Architecture of a Back Propagation Network: The perceptron Model – The solution – Single Layer Artificial Neural Network. Model for Multilayer Perceptron.
Back propagation learning: Input Layer computation – Hidden Layer Computation Output Layer Computation –Calculation of Error– Back Propagation Algorithm.
Reference Book:
1. Fuzzy Logic with Engineering Applications by Timothy J. Ross McGrow Hill 1997. 2.C++ Neural Networks and Fuzzy Logic by Dr.Valluru.B.Rao, Hayagriva, V.Rao. BPB Publications, Second Edition, 1996. 1. Fuzzy set theory -https://www.youtube.com/watch?v=1IjyPXKiK-M 2. Fundamentals of Neural Network -https://www.youtube.com/watch?v=PNqc4fkdfIo
Text Book:
1.S.Rajasekaran, G.A. VijayalakshmiPai, “Neural Networks, Fuzzy Logic and Genetic Algorithms – Synthethesis and Applications “, Prentice Hall of India Pvt. Ltd., New Delhi,2003. Unit I : Chapter 6 Sections:6.1-6.3 Unit II: Chapter 6 Sections: 6.4-6.5 Chapter 7 Sections: 7.1-7.2 Unit III : Chapter 2 sections: 2.1-2.3,2.4-2.41,2.42 Unit IV :,2.9,2.10 Chapter 3 Sections: 3.1(3.1.1-3.1.4) Unit V: 3.2(3.2.1-3.2.4, 3.29)
