{"id":366,"date":"2016-11-04T16:07:45","date_gmt":"2016-11-04T16:07:45","guid":{"rendered":"http:\/\/www.inspirenignite.com\/anna-university\/?p=366"},"modified":"2019-07-17T07:15:47","modified_gmt":"2019-07-17T07:15:47","slug":"anna-university-b-tech-it-r13-6th-soft-computing-detailed-syllabus","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/anna-university\/anna-university-b-tech-it-r13-6th-soft-computing-detailed-syllabus\/","title":{"rendered":"Anna University B.Tech IT (R13) 6th Soft Computing Detailed Syllabus"},"content":{"rendered":"<p>Soft Computing Syllabus for B.Tech 6th sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.<\/p>\n<p>The detailed syllabus for Soft Computing B.Tech (R13) sixthsem is as follows<\/p>\n<p><strong>OBJECTIVES<\/strong>: The student should be made to:<\/p>\n<ul>\n<li>Learn the various soft computing frame works.<\/li>\n<li>Be familiar with design of various neural networks.<\/li>\n<li>Be exposed to fuzzy logic.<\/li>\n<li>Learn genetic programming.<\/li>\n<li>Be exposed to hybrid systems.<\/li>\n<\/ul>\n<p><strong>UNIT I : INTRODUCTION<\/strong> \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 [9 hours]<\/p>\n<p>Artificial neural network: Introduction, characteristics- learning methods \u2013 taxonomy \u2013 Evolution of neural networks- basic models &#8211; important technologies &#8211; applications. Fuzzy logic: Introduction &#8211; crisp sets- fuzzy sets &#8211; crisp relations and fuzzy relations: cartesian product of relation &#8211; classical relation, fuzzy relations, tolerance and equivalence relations, non-iterative fuzzy sets. Genetic algorithm- Introduction &#8211; biological background &#8211; traditional optimization and search techniques &#8211; Genetic basic concepts.<\/p>\n<p><strong>UNIT II : NEURAL NETWORKS \u00a0 \u00a0<\/strong> \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0[9 hours]<\/p>\n<p>McCulloch-Pitts neuron &#8211; linear separability &#8211; hebb network &#8211; supervised learning network: perceptron networks &#8211; adaptive linear neuron, multiple adaptive linear neuron, BPN, RBF, TDNN- associative memory network: auto-associative memory network, hetero-associative memory network, BAM, hopfield networks, iterative autoassociative memory network &amp; iterative associative memory network \u2013unsupervised learning networks: Kohonen self organizing feature maps, LVQ \u2013 CP networks, ART network.<\/p>\n<p><strong>UNIT III : FUZZY LOGIC \u00a0 \u00a0<\/strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0[9 hours]<\/p>\n<p>Membership functions: features, fuzzification, methods of membership value assignments-Defuzzification: lambda cuts &#8211; methods &#8211; fuzzy arithmetic and fuzzy measures: fuzzy arithmetic &#8211; extension principle &#8211; fuzzy measures &#8211; measures of fuzziness -fuzzy integrals &#8211; fuzzy rule base and approximate reasoning : truth values and tables, fuzzy propositions, formation of rules-decomposition of rules, aggregation of fuzzy rules, fuzzy reasoning-fuzzy inference systems-overview of fuzzy expert system-fuzzy decision making.<\/p>\n<p style=\"text-align: center\"><strong><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">Download iStudy<\/a> <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\">Android<\/a><a href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" target=\"_blank\" rel=\"noopener\"> App for complete Anna University syllabus, results, timetables and all other updates. There are no ads and no pdfs and will make your life way easier.<\/a><\/strong><\/p>\n<p><strong>TOTAL: 45 PERIODS <\/strong><\/p>\n<p><strong>OUTCOMES<\/strong>: Upon completion of the course, the student should be able to:<\/p>\n<ul>\n<li>Apply various soft computing frame works.<\/li>\n<li>Design of various neural networks.<\/li>\n<li>Use fuzzy logic.<\/li>\n<li>Apply genetic programming.<\/li>\n<li>Discuss hybrid soft computing.<\/li>\n<\/ul>\n<p><strong>TEXT BOOKS:<\/strong><\/p>\n<p>J.S.R.Jang, C.T. Sun and E.Mizutani, \u201cNeuro-Fuzzy and Soft Computing\u201d, PHI \/ Pearson<br \/>\nEducation 2004.<\/p>\n<p>S.N.Sivanandam and S.N.Deepa, &#8220;Principles of Soft Computing&#8221;, Wiley India Pvt Ltd, 2011.<\/p>\n<p>REFERENCES:<\/p>\n<ul>\n<li>S.Rajasekaran and G.A.Vijayalakshmi Pai, &#8220;Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis &amp; Applications&#8221;, Prentice-Hall of India Pvt. Ltd., 2006.<\/li>\n<li>George J. Klir, Ute St. Clair, Bo Yuan, \u201cFuzzy Set Theory: Foundations and Applications\u201d Prentice Hall, 1997.<\/li>\n<li>David E. Goldberg, \u201cGenetic Algorithm in Search Optimization and Machine Learning\u201d Pearson Education India, 2013.<\/li>\n<li>James A. Freeman, David M. Skapura, \u201cNeural Networks Algorithms, Applications, and Programming Techniques, Pearson Education India, 1991.<\/li>\n<li>Simon Haykin, \u201cNeural Networks Comprehensive Foundation\u201d Second Edition, Pearson Education, 2005.<\/li>\n<\/ul>\n<p>For all other B.Tech IT 6th sem syllabus go to <a href=\"http:\/\/www.inspirenignite.com\/anna-university\/anna-university-b-tech-information-technology-6th-sem-course-structure-for-r13-batch\/\">Anna University B.Tech Information Technology (IT) 6th Sem Course Structure for (R13) Batch<\/a>.\u00a0All details and yearly new syllabus will be updated here time to time.<\/p>\n<p>Do share with friends and in case of questions please feel free drop a comment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Soft Computing Syllabus for B.Tech 6th sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course. The [&hellip;]<\/p>\n","protected":false},"author":2259,"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":[1],"tags":[],"class_list":["post-366","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/366","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/users\/2259"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/comments?post=366"}],"version-history":[{"count":2,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/366\/revisions"}],"predecessor-version":[{"id":10629,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/posts\/366\/revisions\/10629"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/media?parent=366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/categories?post=366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/anna-university\/wp-json\/wp\/v2\/tags?post=366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}