{"id":8855,"date":"2019-12-15T04:52:59","date_gmt":"2019-12-15T04:52:59","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/artificial-neural-networks-and-fuzzy-logic-syllabus-for-vtu-be-2017-scheme-open-elective-2\/"},"modified":"2019-12-15T04:52:59","modified_gmt":"2019-12-15T04:52:59","slug":"artificial-neural-networks-and-fuzzy-logic-syllabus-for-vtu-be-2017-scheme-open-elective-2","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/artificial-neural-networks-and-fuzzy-logic-syllabus-for-vtu-be-2017-scheme-open-elective-2\/","title":{"rendered":"Artificial Neural Networks and Fuzzy Logic Syllabus for VTU BE 2017 Scheme (Open Elective-2)"},"content":{"rendered":"<p>Artificial Neural Networks and Fuzzy Logic detail syllabus for various departments, 2017 scheme is taken from <a href=\"https:\/\/vtu.ac.in\/b-e-scheme-syllabus\/\" target=\"_blank\" rel=\"noopener\">VTU<\/a> official website and presented for VTU students. The course code (17EE661), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n<p>For all other open elective-2 syllabus for vtu be 2017 scheme you can visit <a href=\"..\/open-elective-2-syllabus-for-vtu-be-2017-scheme\">Open Elective-2 syllabus for VTU BE 2017 Scheme Subjects<\/a>. The detail syllabus for artificial neural networks and fuzzy logic is as follows.<\/p>\n<p><h4>Course Objectives:<\/h4>\n<ul>\n<li>To expose the students to the concepts of feed forward neural networks.<\/li>\n<li>To provide adequate knowledge about feedback networks.<\/li>\n<li>To teach about the concept of fuzziness involved in various systems.<\/li>\n<li>To provide adequate knowledge about fuzzy set theory<\/li>\n<\/ul>\n<p><h4>Module 1\t\t\t\t\t<\/h4>\n<p>For complete syllabus and results, class timetable and more pls <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n<p><h4>Module 2<br \/>\n<\/h4>\n<p>Back propagation Networks (continued): Effect of Tuning Parameters of the Back propagation Neural Network, Selection of Various Parameters in BPN, Variations of Standard Back propagation Algorithm. Associative Memory: Auto correlators, Hetero correlators: Kosko&#8217;s Discrete BAM, Wang et al.&#8217;s Multiple Training Encoding Strategy, Exponential BAM, Associative Memory for Real-coded Pattern Pairs, Applications, Recent Trends.\n<\/p>\n<p><h4>Module 3<br \/>\n<\/h4>\n<p>Adaptive Resonance Theory: Introduction, ART l, ART 2, Applications, Sensitivities of Ordering of Data,\n<\/p>\n<p><h4>Module 4<br \/>\nFor complete syllabus and results, class timetable and more pls <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n<p><h4>Module 5<br \/>\n<\/h4>\n<p>Fuzzy Logic And Inference: Crisp Logic, Predicate Logic, Fuzzy Logic, Fuzzy Rule based System, Defuzzification Methods, Applications. Type &#8211; 2 Fuzzy Sets: Representation of Type &#8211; 2 Fuzzy Sets, Operations on Type &#8211; 2 Fuzzy Sets, Interval Type &#8211; 2 Fuzzy Sets.\n<\/p>\n<p><h4>Course Outcomes:<\/h4>\n<p> At the end of the course the student will be able to:<\/p>\n<ul>\n<li>Show an understanding of Organization of the Brain, Biological and Artificial Neuron Models<\/li>\n<li>Show an understanding of Back propagation network architecture, Perceptron Model, Single layer Artificial Neural Network, Model for Multilayer Perceptron, Back propagation Learning,<\/li>\n<li>Show an understanding of Back propagation training and summary of Back propagation Algorithm<\/li>\n<li>Show an understanding Bidirectional Associative Memory (BAM) Architecture<\/li>\n<li>Show an understanding adaptive resonance theory architecture and its applications<\/li>\n<li>Differentiate between crisp logic, predicate logic and fuzzy logic.<\/li>\n<li>Explain fuzzy rule based system<\/li>\n<li>Show an understanding of Defuzzification methods<\/li>\n<\/ul>\n<p><h4>Graduate Attributes (as per NBA):<\/h4>\n<ul>\n<li>Engineering Knowledge,<\/li>\n<li>Problem Analysis,<\/li>\n<\/ul>\n<p><h4>Question paper pattern:<\/h4>\n<ul>\n<li>The question paper will have ten questions.<\/li>\n<li>Each full question is for 16 marks.<\/li>\n<li>There will be 2full questions (with a maximum of four sub questions in one full question) from each module.<\/li>\n<li>Each full question with sub questions will cover the contents under a module.<\/li>\n<li>Students will have to answer 5 full questions, selecting one full question from each module<\/li>\n<\/ul>\n<p><h4>Text Books:<\/h4>\n<ol>\n<li>Neural Networks, Fuzzy Systems and Evolutionary Algorithms: Synthesis and ApplicationsS. Rajasekaran, G.A. VijayalakshmiPai\tPHI Learning\t2nd Edition, 2017<\/li>\n<\/ol>\n<p><h4>Reference Books:<\/h4>\n<ol>\n<li>Neural Networks &#8211; A comprehensive foundation\tSimon Haykin\tPrentice Hall\t3rd Edition, 2004.<\/li>\n<li>Fuzzy Logic With Engineering Applications\tTimothy J Ross\tWiley\t3rd Edition, 2014<\/li>\n<li>Fuzzy sets and Fuzzy Logic: Theory and Applications\tKlir, G.J. Yuan Bo\tPrentice Hall\t2005.<\/li>\n<\/li>\n<\/ol>\n<p>For detail syllabus of all other subjects of BE do syllabus for different schemes from menu given on top.<\/p>\n<p>Dont forget to <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">download iStudy<\/a> for latest syllabus and results, class timetable and more.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial Neural Networks and Fuzzy Logic detail syllabus for various departments, 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EE661), and for [&hellip;]<\/p>\n","protected":false},"author":2298,"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":[15],"tags":[],"class_list":["post-8855","post","type-post","status-publish","format-standard","hentry","category-syllabus"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/posts\/8855","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/users\/2298"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/comments?post=8855"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/posts\/8855\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/media?parent=8855"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/categories?post=8855"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/vtu\/wp-json\/wp\/v2\/tags?post=8855"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}