Network Flows MaxFlow MinCut Theorem amp FordFulkerson Algorithm

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#fuzzyset #maxminComposition #softComputing • Fuzzy composition-Max-min and Max-product compositions • • Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types • of activation function, Network architectures, Knowledge representation, Hebb net • 1.2 Learning processes: Supervised learning, Unsupervised learning and • Reinforcement learning • 1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta • Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule • 1.4 Applications and scope of Neural Networks • 10 • 2 • Supervised Learning Networks : • 2.1 Perception Networks – continuous discrete, Perceptron convergence theorem, • Adaline, Madaline, Method of steepest descent, – least mean square algorithm, • Linear non-linear separable classes Pattern classes, • 2.2 Back Propagation Network, • 2.3 Radial Basis Function Network. • 12 • 3 • Unsupervised learning network: • 3.1 Fixed weights competitive nets, • 3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization, • 3.3 Adaptive Resonance Theory – 1 • 06 • 4 • Associative memory networks: • 4.1 Introduction, Training algorithms for Pattern Association, • 4.2 Auto-associative Memory Network, Hetero-associative Memory Network, • Bidirectional Associative Memory, • 4.3 Discrete Hopfield Networks. • 08 • 5 • Fuzzy Logic: • 5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence • 5.2 Fuzzification and Defuzzification • 5.3 Fuzzy Controllers

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