Neural Networks & Fuzzy Logic Notes

Get ready to learn “Neural Networks & Fuzzy Logic ” by simple and easy handwritten notes that were specially made for B.tech students (CSE). These notes are handwritten Notes of the Computer Subject “Neural Networks & Fuzzy Logic ” unit wise in Pdf format. These notes enables students to understand every concept of the the term “Neural Networks & Fuzzy Logic”.

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UNIT 1.

Introduction: Concepts of neural networks, Characteristics of Neural Networks, Historical Perspective, and Applications of Neural Networks.

Fundamentals of Neural Networks: The biological prototype, Neuron concept, Single layer Neural Networks, Multi-Layer Neural Networks, terminology, Notation and representation of Neural Networks, Training of Artificial Neural Networks.Representation of perceptron and issues, perceptron learning and training, Classification, linear Separability.

Unit 2.

Hopfield nets: Structure, training, and applications, Stability

Back propagation: Concept, Applications, and Back Propagation Training Algorithms. Counter Propagation Networks: Kohonan Network, Grossberg Layer & Training, applications of counter propagation, Image classification.

UNIT 3.

Bi-directional Associative Memories: Structure, retrieving a stored association, encoding associations, memory capacity.ART: ART architecture, ART classification operation, ART implementation, and characteristics of ART.Image Compression Using ART

UNIT 4.

Optical Neural Networks: Vector Matrix Multipliers, Hop field net using Electro optical matrix multipliers, Holographic correlator, Optical Hopfield net using Volume Holograms.The Cognitrons and Neocognitrons: Their structure and training.Genetic Algorithms: Elements, a simple genetic algorithm, working of genetic algorithms evolving neural networks.

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6 thoughts on “Neural Networks & Fuzzy Logic Notes

  1. Thank you so much sir ,,,these notes are very helpful but please upload data mining and data warehousing notes if you have available… Thank you

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