Course Name: Fuzzy Logic and Neural Networks

Course abstract

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.


Course Instructor

Media Object

Prof. Dilip Kumar Pratihar

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 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 240 papers, mostly in various international journals. I have written a textbook on “Soft Computing”, 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” and “Modeling and Analysis of Laser Metal Forming Processes by Finite Element and Soft Computing Methods”. Recently, I have published another textbook named “Fundamentals of Robotics”. My textbook on “Soft Computing” had been translated into Chinese language in 2009. I have guided 18 Ph.D.s. I am in editorial board of 14 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.
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Teaching Assistant(s)

Pushpendra Gupta

P.hD

Amit Kumar Das

P.hD

BONDADA VENKATASAINATH

P.hD

 Course Duration : Feb-Apr 2020

  View Course

 Enrollment : 18-Nov-2019 to 24-Feb-2020

 Exam registration : 16-Dec-2019 to 20-Mar-2020

 Exam Date : 26-Apr-2020

Enrolled

3301

Registered

86

Certificate Eligible

60

Certified Category Count

Gold

5

Silver

25

Elite

20

Successfully completed

10

Participation

4

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Fuzzy Logic and Neural Networks - Toppers list

S KRISHNA ANAND 92%

SREENIDHI INSTITUTE OF SCIENCE AND TECHNOLOGY

B. V. ARUN KIRAN 91%

Homi Bhabha National Institute

BALACHANDRA KUMARASWAMY 91%

BMS College of Engineering

A C ARUN RAJ 90%

SRM Institute of Science and Technology, Kattankulathur Campus.

RAHUL KUMAR 90%

SHRI MATA VAISHNO DEVI UNIVERSITY

Enrollment Statistics

Total Enrollment: 3301

Registration Statistics

Total Registration : 394

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.