{"id":17554,"date":"2020-09-09T11:13:42","date_gmt":"2020-09-09T11:13:42","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/ec-49-c-neural-network-and-fuzzy-logic-syllabus-for-et-8th-sem-2017-dbatu-elective-x\/"},"modified":"2020-09-09T11:13:42","modified_gmt":"2020-09-09T11:13:42","slug":"ec-49-c-neural-network-and-fuzzy-logic-syllabus-for-et-8th-sem-2017-dbatu-elective-x","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/ec-49-c-neural-network-and-fuzzy-logic-syllabus-for-et-8th-sem-2017-dbatu-elective-x\/","title":{"rendered":"EC 49 C: Neural Network and Fuzzy Logic Syllabus for ET 8th Sem 2017 DBATU (Elective-X)"},"content":{"rendered":"

Neural Network and Fuzzy Logic detailed syllabus scheme for Electronics & Telecommunication Engineering (ET), 2017 onwards has been taken from the DBATU<\/a> official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below. <\/p>\n

For 8th Sem Scheme of Electronics & Telecommunication Engineering (ET), 2017 Onwards, do visit ET 8th Sem Scheme, 2017 Onwards<\/a>. For the Elective-X scheme of 8th Sem 2017 onwards, refer to ET 8th Sem Elective-X Scheme 2017 Onwards<\/a>. The detail syllabus for neural network and fuzzy logic is as follows.<\/p>\n

Neural Network and Fuzzy Logic Syllabus for Electronics & Telecommunication Engineering (ET) 4th Year 8th Sem 2017 DBATU<\/h2>\n

Neural Network and Fuzzy logic<\/title><\/p>\n<h4>Course Objectives:<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.<\/b><br \/><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Course Outcomes:<\/h4>\n<ol>\n<li>The student will be able to obtain the fundamentals and types of neural networks.<\/li>\n<li>The student will have a broad knowledge in developing the different algorithms for neural networks.<\/li>\n<li>Student will be able analyze neural controllers.<\/li>\n<li>Student will have a broad knowledge in Fuzzy logic principles.<\/li>\n<li>Student will be able to determine different methods of Deffuzification<\/li>\n<\/ol>\n<h4>Unit 1<\/h4>\n<p> <strong>Introduction<\/strong><br \/>\n Biological neurons, McCulloch and Pitts models of neuron, Types of activation function, Network architectures, Knowledge representation, Learning process: Error-correction learning, Supervised learning, Unsupervised learning, Learning Rules<\/p>\n<h4>Unit 2<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.<\/b><br \/><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Unit 3<\/h4>\n<p> <strong>Multilayer Perception<\/strong><br \/>\n Derivation of the back-propagation algorithm, Learning Factors.<\/p>\n<h4>Unit 4<\/h4>\n<p> <strong>Radial Basis and Recurrent Neural Networks<\/strong><br \/>\n RBF network structure theorem and the reparability of patterns, RBF learning strategies, K-means and LMS algorithms, comparison of RBF and MLP networks, Hopfield networks: energy function, spurious states, error performance.<\/p>\n<h4>Unit 5<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.<\/b><br \/><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a>. <\/p>\n<h4>Unit 6<\/h4>\n<p> <strong>Fuzzy logic<\/strong><br \/>\n Fuzzy sets, Properties, Operations on fuzzy sets, Fuzzy relation Operations on fuzzy relations, The extension principle, Fuzzy mean Membership functions, Fuzzification and defuzzification methods, Fuzzy controllers.<\/p>\n<h4>Reference\/Text Book:<\/h4>\n<ol>\n<li>Simon Haykin, “Neural Network a – Comprehensive Foundation”, Pearson Education.<\/li>\n<li>Dr. S. N. Sivanandam, Mrs S.N. Deepa Introduction to Soft computing tool Wiley Publication.<\/li>\n<li>Satish Kumar Neural Networks: A classroom Approach Tata McGraw-Hill.<\/li>\n<li>Zurada J.M., “Introduction to Artificial Neural Systems, Jaico publishers.<\/li>\n<li>Thimothv J. Ross, “Fuzz V Logic with Engineering Applications”, McGraw.<\/li>\n<li>Ahmad Ibrahim, “Introduction to Applied Fuzzy Electronics’, PHI.<\/li>\n<li>Rajsekaran S, VijaylakshmiPai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, PHI.<\/li>\n<li>Hagan, Demuth, Beale, eNeural Network Design!, Thomson Learning<\/li>\n<li>Christopher M Bishop Neural Networks for Pattern Recognition, Oxford Publication.<\/li>\n<li>William W Hsieh Machine Learning Methods in the Environmental Sciences Neural Network and Kernels Cambridge Publication.<\/li>\n<li>Dr. S. N. Sivanandam, Dr. S. Sumathi Introduction to Neural Network Using Matlab Tata McGraw-Hill<\/li>\n<\/ol>\n<p align=\"justify\">For detail syllabus of all subjects of Electronics & Telecommunication Engineering (ET) 8th Sem 2017 onwards, visit <a href=\"..\/category\/dbatu\/8th-sem-dbatu\">ET 8th Sem Subjects <\/a>of 2017 Onwards.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Neural Network and Fuzzy Logic detailed syllabus scheme for Electronics & Telecommunication Engineering (ET), 2017 onwards has been taken from the DBATU official website and presented for the Bachelor of […]<\/p>\n","protected":false},"author":2351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[108],"tags":[],"class_list":["post-17554","post","type-post","status-publish","format-standard","hentry","category-et-dbatu"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/17554","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/users\/2351"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/comments?post=17554"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/17554\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/media?parent=17554"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/categories?post=17554"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/tags?post=17554"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}