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Neural Computation of Decisions in Optimization Problems

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Unit III Associative LearningOverview Associative learning is the focus of this unit, which begins with an introduction to the concept and its significance in neural networks. You'll study Hopfield networks and their error performance, as well as simulated annealing processes. The unit covers Boltzmann machines and Boltzmann learning, including state transition diagrams and the problem of false minima. Stochastic update methods and simulated annealing are also discussed. Finally, the unit explores basic functional units of Unit IV Competitive learning Neural NetworkOverview This unit delves into competitive learning neural networks. It begins with the components of competitive learning networks and explores pattern clustering and feature mapping networks. You'll learn about Adaptive Resonance Theory (ART) networks, their features, and applications such as character recognition using ART networks. The unit also covers Self-Organizing Maps (SOM), detailing two basic feature mapping models, the SOM algorithm, and the properties of feature maps. Computer simu

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