Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4

    1. 1. Neural Networks and Fuzzy Systems

Neural and Fuzzy Machine Intelligence, Fuzziness as Multivalence, The Dynamical-Systems Approach to Machine Intelligence, Intelligent Behavior as Adaptive Model- Free Estimation.
2.  Neural Dynamics I: Activations and Signals
Neurons as Functions, Signal Monotonicity, Biological Activations and Signals, Neuron Fields,Neuronal Dynamical Systems, Common Signal Functions, Pulse-Coded Signal Functions.
3.   Neuronal Dynamics II: Activation Models
Neuronal Dynamical Systems, Additive Neuronal Dynamics, Additive Neuronal Feedback, Additive Bivalent Models, BAM Connection Matrices, Additive Dynamic and the Noise-Saturation Dilemma, General Neuronal Activations: Cohen-Grossberg and Multiplicative Models.
4.  Synaptic Dynamics I: Unsupervised Learning
Learning as Encoding, Change, and Quantization, Four Unsupervised Learning Laws, Probability Spaces and Random Processes, Stochastic Unsupervised Learning and Stochastic Equilibrium, Signal Hebbian Learning, Competitive Learning, Differential Hebbian Learning, Differential Competitive Leering.
5.  Synaptic Dynamics II: Supervised Learning
Supervised Function Estimation, Supervised Learning as Operant Conditioning, Supervised Learning as Stochastic Pattern Learning with known Class Memberships, Supervised Learning as stochastic Approximation, The Back propagation Algorithm.
6.  Fuzziness Versus Probability
Fuzzy Sets and Systems, Fuzziness in a Probabilistic World, Randomness vs. Ambiguity: Whether vs. How much, The Universe as a Fuzzy Set, The Geometry of Fuzzy Set, The Geometry of Fuzzy Sets: Sets as Points. The Fuzzy Entropy Theorem, The Subsethood theorem. The Entropy-Subsethood Theorem.
7.  Fuzzy Associative Memories
Fuzzy Systems as Between-Cube Mappings, Fuzzy and Neural Function Estimators, Fuzzy Hebb FAMs, Adaptive FAMs: Product-Space Clustering in FAM Cells.

TEXT BOOK: 
Neural Networks & Fuzzy Systems ,  Bark Kosko,  PHI Published in 1994

REFERNCE BOOKS:
1. Fundamentals of Artificial Neural Networks, Mohamad H Hassoum.  PHI
2. Neural network Design, Hagan, Demuth and Beale, Vikas Publishing House
3. Fuzzy Set Theory & its Application, .J. Zimmerman Allied Published Ltd.

tejus mahiCSE 4.1 SyllabusCSE,CSE Syllabus,Neutral Networks & Fuzzy Logic Syllabus
Periods/week : 3 Periods & 1 Tut /week.                                                                  Ses. : 30 Exam : 70 Examination (Practical): 3hrs.                                                                                   Credits: 4 1. Neural Networks and Fuzzy Systems Neural and Fuzzy Machine Intelligence, Fuzziness as Multivalence, The Dynamical-Systems Approach...