{"id":675,"date":"2021-10-12T16:03:30","date_gmt":"2021-10-12T16:03:30","guid":{"rendered":"https:\/\/itgeeks.in\/home\/?p=675"},"modified":"2021-10-14T09:16:45","modified_gmt":"2021-10-14T09:16:45","slug":"2k6-me-805b-neural-networks-fuzzy-logic","status":"publish","type":"post","link":"https:\/\/itgeeks.in\/home\/?p=675","title":{"rendered":"2K6 ME 805(B) \/ 2K6 CS 805(C) : NEURAL NETWORKS & FUZZY LOGIC"},"content":{"rendered":"\n

Module I (13 hours)<\/strong>
Introduction to artificial neural networks – biological neurons – Mc Culloch and Pitts modals of neuron – types of activation function – network architectures – knowledge representation – learning process – error-correction learning – supervised learning – unsupervised learning – single unit mappings and the perceptron – perceptron convergencetheorem (with out proof) – method of steepest descent – least mean square algorithms – adaline\/medaline units – multilayer perceptrons – derivation of the back-propagation algorithm
Module II (13 hours)<\/strong>
Radial basis and recurrent neural networks – RBF network structure – covers theorem and the separability of patterns – RBF learning strategies – K-means and LMS algorithms – comparison of RBF and MLP networks – recurrent networks – Hopfield networks – energy function – spurious states – error performance – simulated annealing – the Boltzman machine – Boltzman learning rule – the mean field theory machine – MFT learning algorithm – applications of neural network – the XOR problem – traveling salesman problem – image compression using MLPs – character retrieval using Hopfield networks
Module III (13 hours)<\/strong>
Fuzzy logic – fuzzy sets – properties – operations on fuzzy sets – fuzzy relations – operations on fuzzy relations – the extension principle – fuzzy measures – membership functions – fuzzification and defuzzification methods – fuzzy controllers – Mamdani and Sugeno types – design parameters – choice of membership functions – fuzzification and defuzzification methods – applications
Module IV (13 hours)<\/strong>
Introduction to genetic algorithm and hybrid systems – genetic algorithms – natural evolution – properties – classification – GA features – coding – selection – reproduction – cross over and mutation operators basic GA and structure Introduction to Hybrid systems – concept of neuro-fuzzy and neuro-genetic system <\/p>\n\n\n\n

Reference books<\/strong>
1 Simon Haykins, \u201cNeural Network a – Comprehensive Foundation\u201d, Macmillan College, Proc, Con, Inc<\/a>
2 Zurada J.M., \u201cIntroduction to Artificial Neural Systems, Jaico publishers<\/a>
3 Driankov D., Hellendoorn H. & Reinfrank M., \u201cAn Introduction to Fuzzy Control\u201d, Norosa Publishing House<\/a>
4 Ross T.J., \u201cFuzzy Logic with Engineering Applications\u201d, McGraw Hill<\/a>
5 Bart Kosko. \u201cNeural Network and Fuzzy Systems\u201d, Prentice Hall, Inc., Englewood Cliffs<\/a>
6 Goldberg D.E., \u201cGenetic Algorithms in Search Optimisation and Machine Learning\u201d, Addison Wesley<\/a>
7 Suran Goonatilake & Sukhdev Khebbal (Eds.), \u201cIntelligent Hybrid Systems\u201d, John Wiley<\/p>\n\n\n\n

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S8 QUESTION PAPERS<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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