ME 697Y Assignment Problem Sets (Spring 2022)

Note: All files are in Word or pdf format.

Syllabus:

Project 1: (in-class presentation Feb. 15 & 17): Choose the one that has not been selected after an announcement is made in class.

You need to submit your short proposal by Feb. 8 (the earlier, the better) and upload the final presentation slides (in power point) and programs in one zipped file by 2:00pm on Feb. 14 into the Brightspace Assignment Project 1.

1. ANFIS: Abd Alrhman Bani Issa
2. FFNN using BP training: Vaidyanath Harinarayana, Akanksha Parmar
3. FFNN for a dynamic system using BP training: Luca Vaccino
4. FFNN using GA:  Rakesh Karunakaran
5. RBFN using LS:  Abd Alrhman Bani Issa
6. RBFN using OLS: Weigang Hou
7. RBFN using OLSGA: Changkuan Liang
8. FBFN using ALS:   Jalil Chavez
9. FBFN using OLS:  Xiaowei Chen
10. Recurrent neural networks or back propagation in time: Pawan Panth
11. CNN: Sijie Zhang, Christopher Morrissey
12. Any other NN paradigm:

Topic 1 and 4 must be done together.

Project 2: (in-class presentation on March 8 & 10):

• Genetic algorithm: Akanksha Parmar, Abd Alrhman Bani Issa
• Evolutionary Strategies (mu, lambda):  Weigang Hou, Sijie Zhang, Vaidyanath
• Evolutionary Strategies (mu + lambda):  Changkuan Liang, Luca Vaccino
• Evolutionary Strategies (mu/r, lambda): 
• Evolutionary Strategies (mu/r + lambda):  Jalil Chavez,
• Extended ES:
• PSO: Pawan Panth, Rakeshkumar
• Support Vector Machines: Chris Morrissey, Xiaowei Chen, Yuan Feng
• Comparison between two optimization methods:
• Any variation of above:

Project 3: (in-class presentation on April 5&7):

• Model reference control using neural networks: Steven Liang, Vaidyanath, Abd Alrhman Bani Issa
• Inverse control using neural networks and fuzzy logic:
• Model predictive control using neural (or fuzzy) networks: Jalil Chavez, Luca Vaccino, Xiaowei Chen
• Self tuning adaptive control using neural networks: Pawan Panth
• Fuzzy PID supervisory control of robot arms: Chris Morrissey
• Fuzzy PID supervisory control: Yuan Feng, Akanksha Parmar
• Fuzzy PID control: Weigang Hou, Sijie Zhang, Rakeshkumar
• Hybrid fuzzy control: 
• Fuzzy model reference learning control: 
• Fuzzy internal model control:
• Fuzzy model optimal control:
• Self organizing fuzzy PID control:

Presentation Outline

1. Problem definition (with mathematical function or real data)

2. Training results

3. Test results

4. Graphical approximation results (compared with the original function) if possible

5. Conclusions

Items to be submitted

1. Presentation slides (power point slides)

2. Programs (in zipped format) with the instruction on how to run the programs

Final Term Project

1. Proposal due date:  March 27, 2022 (download the guideline)
2. Topic: your choice (can be an extension of earlier work, combination of multiple, a new one)
3. In-class presentation:  April 26 and 28 (15 min per person)
4. Due date (program and final report):  5:00 pm on April 29.

See the guideline of the final report.