PRODUCT DESIGN TIME FORECAST USING RELATIVE ENTROPY KERNEL REGRESSION

Authors

  • Hong-Sen Yan Southeast University
  • Zhi-Gen Shang Yancheng Institute of Technology

DOI:

https://doi.org/10.23055/ijietap.2019.26.3.2100

Keywords:

industrial engineering, manufacturing engineering, technology management

Abstract

Design time forecast is characterized by problems of small samples and heteroscedastic noise. For better control of product development process, the forecast method is expected to yield not only highly precise forecast values but valid forecast variances. For this end, relative entropy kernel regression (REKR) is proposed based on the combination of kernel-based regression and Gaussian margin machines (GMM). REKR maintains a Gaussian distribution over weight vectors for the kernel-based regression to minimize the combination of the relative entropy and the negative log probability densities of the targets. To simplify the formulation of REKR, the covariance matrix of Gaussian distribution is set as a diagonal matrix. The simplified optimization problem of REKR is solved based on particle swarm optimization (PSO). Having inherited the benefits of GMM, REKR can simultaneously offer the desirable forecast value and forecast variance. Experiments on the time forecast of plastic injection mold design as well as on both synthetic and real datasets do verify the feasibility and validity of REKR.

Author Biographies

Hong-Sen Yan, Southeast University

Hong-Sen Yan received the B.S. degree in automatic control from the Harbin Ship Building Engineering Institute, Harbin, China, in 1982 and the M.S. degree in industrial automation and the Ph.D. degree in automatic control theory and application from the Harbin Institute of Technology, Harbin, in 1989 and 1992, respectively.

From February 1982 to August 1986, he was an Assistant Engineer with the Yangzhou Marine Electronic Instruments Institute, Yangzhou, China. From July 1992 to June 1994, he was a Postdoctoral Fellow with the CIMS Laboratory, Department of Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. From July 1994 to March 1998, he was an Associate Professor with the Research Institute of Automation, Southeast University, Nanjing, where he was a Professor from April 1998 to September 2007. Since October 2007, he has been a Professor with the MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering, and the Superintendent of the Research Institute of Control and Optimization of Manufacturing Systems, School of Automation, Southeast University. He is the founder of knowledgeable manufacturing and knowledge meshes theory. He has been engaged in or finished the design and development of four computer-integrated manufacturing systems in enterprises with his colleagues. He has authored or coauthored more than 200 research papers in refereed journals such as Applied Artificial Intelligence, Computers and Industrial Engineering, Computers in Industry, Concurrent Engineering: Research & Applications, European Journal of Operational Research, Expert Systems with Applications, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Engineering Management, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks, IEEE Transactions on Systems, Man and Cybernetics, Part A, IIE Transactions, Information Sciences, International Journal of Advanced Manufacturing Technology, International Journal of Computer Integrated Manufacturing, International Journal of Industrial Engineering, International Journal of Production Research, Journal of Intelligent and Fuzzy Systems, Journal of Intelligent Manufacturing, Journal of Optimization Theory and Applications, Proceedings of IMechE Part B: Journal of Engineering Manufacture, and Robotics and Computer-Integrated Manufacturing. His research interests include flexible manufacturing systems, computer integrated manufacturing systems (CIMS), agile manufacturing, concurrent engineering, and knowledgeable manufacturing systems.

Dr. Yan is a Senior Member of the Institute of Industrial Engineers, a Director of the Jiangsu Association of Automation in China, and a Senior Member of the Chinese Mechanical Engineering Society. He was a director of the Chinese Association for Artificial Intelligence. He won the Best Journal Paper Prize for one of the best 200 papers selected from the 2001–2005 years’ papers in the 283 journals sponsored by 105 institutes, societies, institutions, and associations of the China Association for Science and Technology, and also seven other prizes from the State, the ministry, the province, and universities.

Zhi-Gen Shang, Yancheng Institute of Technology

Zhi-Gen Shang received the Ph.D. degree in control theory and engineering from the School of Automation, Southeast University, Nanjing, China in 2013. Currently, he is a lecturer in the Department of Automation at Yancheng Institute of Technology. He has authored or coauthored some research papers in refereed journals such as Applied Artificial Intelligence, and Neurocomputing. His research interests include intelligent algorithm, and applications of forecasting technology.

Published

2019-07-20

How to Cite

Yan, H.-S., & Shang, Z.-G. (2019). PRODUCT DESIGN TIME FORECAST USING RELATIVE ENTROPY KERNEL REGRESSION. International Journal of Industrial Engineering: Theory, Applications and Practice, 26(3). https://doi.org/10.23055/ijietap.2019.26.3.2100

Issue

Section

Work Measurement, Human Factors and Ergonomics

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