Fundamentals of Neural Networks: Basic concepts of Neural networks, Human Brain, Model of an Artificial Neuron, Neural network architectures, Characteristics of Neural Networks, Learning methods, Taxonomy of Neural Network Architectures, Early Neural Network Architectures.
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, Illustration, Applications.
Revised Bloom’s Taxonomy Level L1 – Remembering, L2 – Understanding, L3 – Applying.
Back propagation Networks (continued): Effect of Tuning Parameters of the Back propagation Neural Network, Selection of Various Parameters in BPN, Variations of Standard Back propagation Algorithm.
Associative Memory: Auto correlators, Hetero correlators: Kosko's Discrete BAM, Wang et al.'s Multiple Training Encoding Strategy, Exponential BAM, Associative Memory for Real-coded Pattern Pairs, Applications, Recent Trends.
Revised Bloom’s Taxonomy Level L1 – Remembering, L2 – Understanding, L3 – Applying.
Adaptive Resonance Theory: Introduction, ART l, ART 2, Applications, Sensitivities of Ordering of Data.
Revised Bloom’s Taxonomy Level L1 – Remembering, L2 – Understanding, L3 – Applying.
Fuzzy Set Theory: Fuzzy versus Crisp, Crisp sets, Fuzzy Sets, Crisp Relations, FuzzyRelations.
Revised Bloom’s Taxonomy Level L1 – Remembering, L2 – Understanding. L3 – Applying.
Fuzzy Logic And Inference: Crisp Logic, Predicate Logic, Fuzzy Logic, Fuzzy Rule based System, Defuzzification Methods, Applications.
Type – 2 Fuzzy Sets: Representation of Type – 2 Fuzzy Sets, Operations on Type – 2 Fuzzy Sets, Interval Type – 2 Fuzzy Sets.
Revised Bloom’s Taxonomy Level L1 – Remembering, L2 – Understanding. L3 – Applying.