{"id":19476,"date":"2020-09-13T07:50:27","date_gmt":"2020-09-13T07:50:27","guid":{"rendered":"https:\/\/www.inspirenignite.com\/mh\/btetpe704c-soft-computing-syllabus-for-et-7th-sem-2020-21-dbatu-elective-v-labs\/"},"modified":"2020-09-13T07:50:27","modified_gmt":"2020-09-13T07:50:27","slug":"btetpe704c-soft-computing-syllabus-for-et-7th-sem-2020-21-dbatu-elective-v-labs","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/mh\/btetpe704c-soft-computing-syllabus-for-et-7th-sem-2020-21-dbatu-elective-v-labs\/","title":{"rendered":"BTETPE704C: Soft Computing Syllabus for ET 7th Sem 2020-21 DBATU (Elective-V Labs)"},"content":{"rendered":"

Soft Computing detailed syllabus scheme for Electronics & Telecommunication Engineering (ET), 2020-21 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 7th Sem Scheme of Electronics & Telecommunication Engineering (ET), 2020-21 Onwards, do visit ET 7th Sem Scheme, 2020-21 Onwards<\/a>. For the Elective-V Labs scheme of 7th Sem 2020-21 onwards, refer to ET 7th Sem Elective-V Labs Scheme 2020-21 Onwards<\/a>. The detail syllabus for soft computing is as follows.<\/p>\n

Soft Computing Syllabus for Electronics & Telecommunication Engineering (ET) 4th Year 7th Sem 2020-21 DBATU<\/h2>\n

Soft Computing<\/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<p> After the successful completion of this course, students will be able to:<\/p>\n<ol>\n<li>Use a new tool \/tools to solve a wide variety of real world problems.<\/li>\n<li>Find an alternate solution, which may offer more adaptability, resilience and optimization.<\/li>\n<li>Identify the suitable antenna for a given communication system.<\/li>\n<li>Gain knowledge of soft computing domain which opens up a whole new career option.<\/li>\n<li>Tackle real world research problems.<\/li>\n<\/ol>\n<h4>UNIT – 1<\/h4>\n<p> Artificial Neural Network -I: Biological neuron, Artificial neuron model, concept of bias and threshold, McCulloch- Pits Neuron Model, implementation of logical AND, OR, XOR functions Soft Topologies of neural networks, learning paradigms: supervised, unsupervised, reinforcement, Linear neuron model: concept of error energy, gradient descent algorithm and application of linear neuron for linear regression, Activation functions: binary, bipolar (linear, signup, log sigmoid, tan sigmoid)Learning mechanisms: Hebbian, Delta Rule o Perceptron and its limitations Draft.<\/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> Fuzzy Logic -I: Concept of Fuzzy number, fuzzy set theory (continuous, discrete) o Operations on fuzzy sets, Fuzzy membership functions (core, boundary, and support), primary and composite linguistic terms, Concept of fuzzy relation, composition operation (T-norm,T-conorm) o Fuzzy if-then rules.<\/p>\n<h4>UNIT – 4<\/h4>\n<p> Fuzzy Logic -II: Fuzzification, Membership Value Assignment techniques, De-fuzzification (Max membership principle, Centroid method, Weighted average method), Concept of fuzzy inference, Implication rules- Dienes-Rescher Implication, Mamdani Implication, Zadeh Implication, Fuzzy Inference systems -Mamdani fuzzy model, Sugeno fuzzy model , Tsukamoto fuzzy model, Implementation of a simple two-input single output FIS employing Mamdani model Computing.<\/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> Adaptive Neuro-Fuzzy Inference Systems (ANFIS): ANFIS architecture, Hybrid Learning Algorithm, Advantages and Limitations of ANFIS Application of ANFIS\/CANFIS for regression.<\/p>\n<h4>Text Books:<\/h4>\n<ol>\n<li>Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Laurene Fausett, Pearson Education, Inc, 2008.<\/li>\n<li>Fuzzy Logic with Engineering Applications, Third Edition Thomas, Timothy Ross, John Wiley & Sons, 2010.<\/li>\n<li>Neuro- Fuzzy and Soft Computing, J.S. Jang, C.T. Sun, E. Mizutani, PHI Learning Private Limited.<\/li>\n<li>Principles of Soft Computing, S. N. Sivanandam, S. N. Deepa, John Wiley & Sons, 2007.<\/li>\n<li>Introduction to the theory of neural computation, John Hertz, Anders Krogh, Richard Palmer, Addison -Wesley Publishing Company, 1991.<\/li>\n<li>Neural Networks A comprehensive foundation,, Simon Haykin, Prentice Hall International Inc-1999.<\/li>\n<li>Neural and Adaptive Systems: Fundamentals through Simulations, Jose C. Principe Neil R. Euliano, W. Curt Lefebvre, John-Wiley & Sons, 2000.<\/li>\n<li>Pattern Classification, Peter E. Hart, David G. Stork Richard O. Duda, Second Edition, 2000.<\/li>\n<li>Pattern Recognition, Sergios Theodoridis, Konstantinos Koutroumbas, Fourth Edition, Academic Press, 2008.<\/li>\n<li>A First Course in Fuzzy Logic, Third Edition, Hung T. Nguyen, Elbert A. Walker, Taylor & Francis Group, LLC, 2008.<\/li>\n<li>Introduction to Fuzzy Logic using MATLAB, S. N. Sivanandam, S. Sumathi, S. N. Deepa, Springer Verlag, 2007.<\/li>\n<\/ol>\n<p align=\"justify\">For detail syllabus of all subjects of Electronics & Telecommunication Engineering (ET) 7th Sem 2020-21 onwards, visit <a href=\"..\/category\/dbatu\/7th-sem-dbatu\">ET 7th Sem Subjects <\/a>of 2020-21 Onwards.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Soft Computing detailed syllabus scheme for Electronics & Telecommunication Engineering (ET), 2020-21 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For […]<\/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-19476","post","type-post","status-publish","format-standard","hentry","category-et-dbatu"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/19476","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=19476"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/posts\/19476\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/media?parent=19476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/categories?post=19476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/mh\/wp-json\/wp\/v2\/tags?post=19476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}