This the Syllabus:Module No. 1 Neural Networks Backpropagation network, Backpropagation History, Mathematical model of neuron, ANN architectures, Learning rules, LearningParadigms. Perceptron network,learning and its applications,Variants of BPA.Module No. 2Associative Memory: Auto Hetero, Exponential BAM, Applications. a) Train the autoassociative network for input vector [-1 1 1 1] and test the network for the4same input vector. Test the autoassociative network with one missing, one mistake, twomissing and two mistake entries in test vector.b) Train a heteroassociative memory network using Hebb rule to store row vectors=(S1, S2, S3, S4) to the output row vector t =(t1, t2). The vector pairs are given below:Input targets s,S2t,1st1112nd1113rd1114th0 0 1 1 0 1

Here we have given the solution for two questions , one using autoassociative network and other…

Answered: a) Train the autoassociative network…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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This the Syllabus: Module No. 1 Neural Networks Backpropagation network, Backpropagation History, Mathematical model of neuron, ANN architectures, Learning rules, Learning Paradigms. Perceptron network, learning and its applications,Variants of BPA. Module No. 2 Associative Memory: Auto Hetero, Exponential BAM, Applications.