Asee peer logo

Function Approximation through an Efficient Neural Networks Method

Download Paper |


2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

Course Transformation in ECE

Tagged Division

Electrical and Computer

Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


Chaomin Luo Mississippi State University Orcid 16x16

visit author page

Dr. Chaomin Luo received his Ph.D. in Department of Electrical and Computer Engineering at University of Waterloo, in 2008, his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. in Electrical Engineering from Southeast University, Nanjing, China. He is currently Associate Professor in the Department of Electrical and Computer Engineering, at the Mississippi State University (MSU). He was panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program and panelist in 2017 NSF GRFP Panelist program. He was the General Co-Chair of 2015 IEEE International Workshop on Computational Intelligence in Smart Technologies, and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies, Cleveland, OH. Currently, he is Associate Editor of International Journal of Robotics and Automation, and International Journal of Swarm Intelligence Research. He was the Publicity Chair in 2011 IEEE International Conference on Automation and Logistics. He was on the Conference Committee in 2012 International Conference on Information and Automation and International Symposium on Biomedical Engineering and Publicity Chair in 2012 IEEE International Conference on Automation and Logistics. He was a Chair of IEEE SEM - Computational Intelligence Chapter; a Vice Chair of IEEE SEM- Robotics and Automation and Chair of Education Committee of IEEE SEM. He has extensively published in reputed journal and conference proceedings, such as IEEE Transactions on Neural Networks, IEEE Transactions on SMC, IEEE-ICRA, and IEEE-IROS, etc. His research interests include engineering education, computational intelligence, intelligent systems and control, robotics and autonomous systems, and applied artificial intelligence and machine learning for autonomous systems. He received the Best Paper Award in the IEEE International Conference on Information and Automation (IEEE ICIA2017). He is an ASEE, INFORMS, and IEEE member. He is currently an Associate Editor of The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE-IROS 2019) .

visit author page


Zhuming Bi P.E. Purdue University Fort Wayne Orcid 16x16

visit author page

Zhuming Bi is a Professor of Mechanical Engineering at the Department of Civil and Mechanical Engineering, Purdue University Fort Wayne, USA. He served as a Senior Engineer at National Institute of Standards and Technology (NIST) of USA (2016), a Senior Project Engineer at Northern Ireland Technology Centre, Queen’s University Belfast of UK (2007 – 2009), a Research Scientist at the Integrated Manufacturing Technologies Institute of National Research Council Canada (2003 – 2007), a NSERC Postdoctoral Fellow at Simon Fraser University, Burnaby, BC, Canada (2002 – 2003), and a Visiting Scholar to Nanyang Technological University (2001) and City University of Hong Kong (1997-1998). He was as an Associate Professor at the Department of Manufacturing Engineering, Nanjing University of Science and Technology in China (1996 -1999).
He received a Ph.D. degree in Design and Manufacturing from the University of Saskatchewan in Saskatoon of Canada (2002), and a Ph.D. degree in Mechatronic Control and Automation from Harbin Institute of Technology in China (1994). Dr. Bi’s research interests are Robotics and Automation, Internet of Things (IoT), Enterprise Systems, and Sustainable Manufacturing. He has published 110 international journal articles with 1749 times of citations by others in Web of Science in these research fields. He was the only awardee for the ‘Outstanding Faculty in Research’ and ‘Featured Faculty in Research Endeavors’ at Purdue University Fort Wayne in 2016-2017. He received IEEE Region 4 Outstanding Professional Award in 2018.

visit author page


Wenbing Zhao Cleveland State University Orcid 16x16

visit author page

Dr. Zhao is a Full Professor at the Department of Electrical Engineering and Computer Science, Cleveland State University (CSU). He earned his Ph.D. at University of California, Santa Barbara in 2002. Dr. Zhao has a Bachelor of Science degree in Physics in 1990, and a Master of Science degree in Physics in 1993, both at Peking University, Beijing, China. Dr. Zhao also received a Master of Science degree in Electrical and Computer Engineering in 1998 at University of California, Santa Barbara. Dr. Zhao joined CSU faculty in 2004. He is currently serving as the director of the Master of Science in Electrical Engineering, and the Chair of the Graduate Program Committee in the Department of EECS, the ABET coordinator for the BS in Computer Science Program, and a member of the faculty senate at CSU. Dr. Zhao has authored a research monograph titled: “Building Dependable Distributed Systems” published by Scrivener Publishing, an imprint of John Wiley and Sons. Furthermore, Dr. Zhao published over 200 peer-reviewed papers on fault tolerant and dependable systems (three of them won the best paper award), computer vision and motion analysis, physics, and education. Dr. Zhao’s research is supported in part by the US National Science Foundation, the US Department of Transportation, Ohio State Bureau of Workers’ Compensation, and by Cleveland State University. Dr. Zhao has served on the organizing committee and the technical program committee for numerous international conferences. Dr. Zhao is an Associate Editor for IEEE Access, an Academic Editor for PeerJ Computer Science, and is a member of the editorial board for International Journal of Parallel Emergent and Distributed Systems, International Journal of Distributed Systems and Technologies, International Journal of Performability Engineering, International Journal of Handheld Computing Research. Dr. Zhao is a senior member of IEEE.

visit author page

Download Paper |


Neural network system, a portion of artificial intelligence, is increasingly becoming prevalent nowadays. The application of neural networks models to function approximation is one of the latest developments in electrical engineering including robotics route planning. It is, however, a challenging task to instruct on this topic in artificial intelligence courses. In general, a function approximation issue aims to select a function among a well-defined class that closely matches a target function in a task-specific manner, which has a large number of applications in engineering such as the robotics route planning. In this paper, we present how we follow the pedagogies of sparrow-dissection and scaffolding and help students design, implement, debug, and operate an efficient neural networks model method for function approximation. A back propagation neural networks model is taught in the classroom with the source code provided to students. The students are required to revise and modify the source code therefore apply the back propagation neural networks model for the function approximation. Specifically, in our Artificial Intelligence Techniques course for senior and graduate students, we provide a back propagation neural networks model with its source code as a ‘sparrow’ for students to dissect and examine how the neural networks model for function approximation works. We then work together with students in revision and modification of the source code for purpose of function approximation. With the gradual withdrawal of the instructor in a series of projects assigned to students on the application of back propagation neural networks model to function approximation in robot path planning, students gain more independence and can complete the projects fully by themselves in the end. A set of projects are assigned to students to perform the neural networks model for the function approximation. An assessment approach is given to determine how comfortable students are with neural network concepts before and after the project. In addition, feedback was solicited after each neural network models to get feedback from students about implementation, development and application of the models into function approximation.

The effectiveness of the neural network model for function approximation is evaluated through various milestone assignments, lab reports, presentations, and other activities. In addition, we survey students for their feedback on how comfortable they are with neural network concepts before and after each project, as well as feedback on the process of development and application of each neural networks model by revising the first source code of neural network model. Teaching and learning strategies by the neural network model methodology were associated with learning outcomes of this course by analysis of the function approximation project. All these data, together with from those from the official course evaluation system, point to the effectiveness of the neural network model approach and high learning quality as a result of our sparrow-dissection and scaffolding pedagogies.

Luo, C., & Bi, Z., & Zhao, W. (2019, June), Function Approximation through an Efficient Neural Networks Method Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32867

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2019 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015