Neural Network detail syllabus for Instrumentation And Control, effective from 2019-2020, is collected from BTEUP 2017 Syllabus official website and presented for diploma students. PDF download is possible from official site but you can download the istudy mobile app for syllabus on mobile. The course details such as 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.
For all other bteup syllabus 6th sem instrumentation & ctrl 2019-2020 you can visit BTEUP Syllabus 6th Sem Instrumentation & Ctrl 2019-2020 Subjects. For all other elective subjects do refer to Electives. The detail syllabus for neural network is as follows.
Rationale:
This course introduces the basic models, learning algorithms, and some applications of neural networks. After this course, we should be able to know how to use neural networks for solving different problems related to pattern recognition, function approximation, data visualization, and so on.
Learning Outcomes:
After undergoing the subject, the students will be able to:
- Understand Basic neuron models, application classification and Basic approach of the working of ANN
- Identify Supervised Learning Single-layer Networks
- Understand Unsupervised Learning
- Differentiate different networks
- Learn Basic application like Pattern recognition, function approximation, information visualization, etc
1: Fundamentals of Neural Networks:
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
2: Supervised Learning:
Single-layer Networks, Linear Separability, handling linearly non-separable sets. Training algorithm. Error correction & gradient decent rules.
Multi-layer network- Architecture, Back Propagation Algorithm (BPA) – Various parameters and their selection, Applications, Feedforward Network, Radial- Basis Function (RBF) network & its learning strategies.
3: Unsupervised Learning:
Winner-takes all Networks, Hamming Networks. Adaptive Resonance Theory, Kohonen’s Self organizing Maps.
4: Neurodynamical models:
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
5: Applications:
Pattern recognition, function approximation, information visualization.
Text Books:
- Satish Kumar – Neural Network: A classroom approach
- Jacek M.Zurada- Artificial Neural Networks
- Simon Haykin- Artifical Neural Network
For detail syllabus of all other subjects of BE Instrumentation & Ctrl, 2019-2020 scheme do visit Instrumentation & Ctrl 6 syllabus for 2019-2020 Scheme.
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