Fuzzy Logic and Neural Networks

By Prof. Dilip Kumar Pratihar   |   IIT Kharagpur
Learners enrolled: 1502
This course will start with a brief introduction to fuzzy sets. The differences between fuzzy sets and crisp sets will be identified. Various terms used in the fuzzy sets and the grammar of fuzzy sets will be discussed, in detail, with the help of some numerical examples. The working principles of two most popular applications of fuzzy sets, namely fuzzy reasoning and fuzzy clustering will be explained, and numerical examples will be solved. Fundamentals of neural networks and various learning methods will then be discussed. The principles of multi-layer feed  forward neural network, radial basis function network, self-organizing map, counter-propagation neural network, recurrent neural network, deep learning neural network will be explained with appropriate numerical examples. The method of evolving optimized fuzzy reasoning tools, neural networks will be discussed with the help of some numerical examples. Two popular neuro-fuzzy systems will be explained and numerical examples will be solved. A summary of the course will be given at the end.

   :Students belonging to all disciplines of Engineering, Researchers and practicing Engineers can take this course.
PRE-REQUISITES           :Nil
INDUSTRY SUPPORT    :RDCIS, Ranchi CMERI, Durgapur Reliance Industries, Mumbai C-DAC, Kolkata, and others
Course Status : Completed
Course Type : Elective
Duration : 8 weeks
Category :
  • Multidisciplinary
Credit Points : 2
Level : Undergraduate/Postgraduate
Start Date : 21 Feb 2022
End Date : 15 Apr 2022
Enrollment Ends : 21 Feb 2022
Exam Date : 24 Apr 2022 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1  :  Introduction to Fuzzy Sets
Week 2  :  Introduction to Fuzzy Sets (contd.); Fuzzy reasoning
Week 3  :  Fuzzy reasoning (contd.); Fuzzy clustering
Week 4  :  Fuzzy clustering (contd.); Fundamentals of Neural Networks
Week 5  :  Multi-layer Feed-Forward Neural Network; Radial Basis FunctionNetwork
Week 6  :  Self-Organizing Map; Counter-Propagation Neural Network;Recurrent Neural Networks; Deep Learning Neural Network
Week 7  :  Genetic-Fuzzy system; Genetic-Neural System
Week 8  :  Neuro-Fuzzy System; Concepts of Soft Computing andComputational Intelligence; Summary of the Course

Books and references

•  Soft Computing: Fundamentals and Applications by D.K.Pratihar, Narosa Publishing House, New-Delhi, 2014

•  Fuzzy Sets and Fuzzy Logic: Theory and Applications byGeorge J. Klir, Bo Yuan, Prentice Hall, 1995

•  Neural Networks: A Comprehensive Foundation by S. Haykin,Prentice Hall PTR Upper Saddle River, NJ, USA, 1994   

Instructor bio

Prof. Dilip Kumar Pratihar

IIT Kharagpur
I received BE (Hons.) and M. Tech. from REC (NIT) Durgapur, India, in 1988 and 1994, respectively. I obtained my Ph.D. from IIT Kanpur, India, in 2000. I received University Gold Medal, A.M. Das Memorial Medal, Institution of Engineers’ (I) Medal, and others. I completed my post-doctoral studies in Japan and then, in Germany under the Alexander von Humboldt Fellowship Programme. I received Shastri Fellowship (Indo-Canadian) in 2019 and INSA Teachers’ Award 2020. I am working now as a Professor (HAG scale) of IIT Kharagpur, India. My research areas include robotics, soft computing and manufacturing science. I have published more than 275 papers and book-chapters. I have written the textbooks on “Soft Computing” and “Fundamentals of Robotics”, co-authored another textbook on “Analytical Engineering Mechanics”, edited a book on “Intelligent and Autonomous Systems”, co-authored reference books on “Modeling and Analysis of Six- legged Robots”; “Modeling and Simulations of Robotic Systems Using Soft Computing”; “Modeling and Analysis of Laser Metal Forming Processes by Finite Element and Soft Computing Methods” and “Multibody Dynamic Modeling of Multi-legged Robots”. My textbook on “Soft Computing” had been translated into Chinese language in 2009. I have guided 22 Ph.D.s. I am in editorial board of 10 International Journals. I have been elected as FIE, MASME and SMIEEE. I have completed a few sponsored (funded by DST, DAE, MHRD) and consultancy projects. I have filed two patents.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 24 April 2022 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kharagpur .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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