{"id":7020,"date":"2019-12-14T16:49:20","date_gmt":"2019-12-14T16:49:20","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/neural-network-and-fuzzy-logic-systems-eie-8th-sem-syllabus-for-vtu-be-2017-scheme\/"},"modified":"2019-12-14T16:49:20","modified_gmt":"2019-12-14T16:49:20","slug":"neural-network-and-fuzzy-logic-systems-eie-8th-sem-syllabus-for-vtu-be-2017-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/neural-network-and-fuzzy-logic-systems-eie-8th-sem-syllabus-for-vtu-be-2017-scheme\/","title":{"rendered":"Neural Network and Fuzzy Logic Systems EIE 8th Sem Syllabus for VTU BE 2017 Scheme"},"content":{"rendered":"

Neural Network and Fuzzy Logic Systems detail syllabus for Electronics & Instrumentation Engineering (Eie), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17EI81), 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

For all other eie 8th sem syllabus for be 2017 scheme vtu you can visit EIE 8th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. The detail syllabus for neural network and fuzzy logic systems is as follows.<\/p>\n

Module 1 <\/h4>\n

Introduction. – Neural Networks, Application Scope of Neural Networks, Fuzzy Logic, Generic Algorithm, Hybrid Systems, Soft Computing. Artificial Neural Network: An Introduction. – Fundamental Concept, Evolution of Neural Networks, Basic models of Artificial Neural Networks (ANN), Important Technologies of ANNs, McCulloch-Pitts Neuron, Linear Separability.\n<\/p>\n

Module 2 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3 <\/h4>\n

Back -Propagation Network. – Theory, Architecture, Flowchart for training process, Training Algorithm, Learning Factors of Back-Propagation Network, Testing Algorithm of Back-Propagation Network. Radial Basis Function Network, Time Delay Neural Network, Functional Link Networks, Tree Neural Networks, wavelet neural network.\n<\/p>\n

Module 4 <\/h4>\n

Introduction to Fuzzy Logic, Classical sets and Fuzzy sets. Introduction to Fuzzy Logic, Classical sets (crisp sets) – Operations on Classical sets, Properties of Classical sets, Function of Mapping of Classical sets. Fuzzy sets – Fuzzy set operations, Properties of fuzzy sets. Simple Problems Classical Relations and Fuzzy Relations – Introduction, Cartesian Product of Relation, Classical Relation, Fuzzy Relation, Tolerance and Equivalence Relations, Non-interactive Fuzzy sets, Simple Problems.\n<\/p>\n

Module 5 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to:<\/p>\n

    \n
  1. Compare and contrast the biological neural network and ANN.<\/li>\n
  2. Discuss the ANN for pattern classification.<\/li>\n
  3. Develop and configure ANNs with different types of functions and learning algorithms.<\/li>\n
  4. Apply ANN for real world problems.<\/li>\n
  5. Discuss the fundamentals of fuzzy logic, implementation and their functions<\/li>\n
  6. Apply fuzzy logic concepts in building automated control systems.<\/li>\n<\/ol>\n

    Question paper pattern:<\/h4>\n