Abstract
Product conceptual design is an innovative activity that is to form and optimize the projects of products. Identification of the best conceptual design candidate is a crucial step as design information is not complete and design knowledge is minimal at conceptual design stage. It is necessary to select the best scheme from feasible alternatives through comparison and filter. In this paper, the evaluation system of mechanism scheme is established firstly based on the performance analysis of the mechanism system and the opinions of experts. Then, the fuzzy optimum selection model of mechanism scheme evaluation is provided. Combined with the fuzzy optimum selection model with the neural network theory, a rational pattern of determining the topologic structure of network is provided. It also provides a weight-adjusted BP model of the neural network with the fuzzy optimum selection model for mechanism scheme. Finally, an example is given to verify the effective feasibility of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, H.Z., Zuo, M.J., Sun, Z.Q.: Bayesian Reliability Analysis for Fuzzy Lifetime Data. Fuzzy Sets and Systems 157, 1674–1686 (2006)
Huang, H.Z., Wang, P., Zuo, M.J., Wu, W.D., Liu, C.S.: A Fuzzy Set Based Solution Method for Multiobjective Optimal Design Problem of Mechanical and Structural Systems Using Functional-Link Net. Neural Computing & Applications 15, 239–244 (2006)
Huang, H.Z., Wu, W.D., Liu, C.S.: A Coordination Method for Fuzzy Multi-ObjectiveOptimization of System Reliability. Journal of Intelligent and Fuzzy Systems 16, 213–220 (2005)
Huang, H.Z., Li, H.B.: Perturbation Fuzzy Finite Element Method of Structural Analysis Based on Variational Principle. Engineering Applications of Artificial Intelligence 18, 83–91 (2005)
Huang, H.Z., Tong, X., Zuo, M.J.: Posbist Fault Tree Analysis of Coherent Systems. Reliability Engineering and System Safety 84, 141–148 (2004)
Huang, H.Z.: Fuzzy Multi-Objective Optimization Decision-Making of Reliability of Series System. Microelectronics and Reliability 37, 447–449 (1997)
Huang, H.Z.: Reliability Analysis Method in the Presence of Fuzziness Attached to Operating Time. Microelectronics and Reliability 35, 1483–1487 (1995)
Zhang, Z., Huang, H.Z., Yu, L.F.: Fuzzy Preference Based Interactive Fuzzy Physical Programming and Its Application in Multi-objective Optimization. Journal of Mechanical Science and Technology 20, 731–737 (2006)
Xue, L.-H., Huang, H.-Z., Hu, J., Miao, Q., Ling, D.: RAOGA-Based Fuzzy Neural Network Model of Design Evaluation. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNAI), vol. 4114, pp. 206–211. Springer, Heidelberg (2006)
Huang, H.Z., Tian, Z.G.: Application of Neural Network to Interactive Physical Programming. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 725–730. Springer, Heidelberg (2005)
Li, H.B., Huang, H.Z., Zhao, M.Y.: Finite Element Analysis of Structures Based on Linear Saturated System Model. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 820–825. Springer, Heidelberg (2004)
Song, Y.Y., Cai, F.Z., Zhang, B.P.: One of Case-Based Reasoning Product Conceptual Design Systems. Journal of Tsinghua University 38, 5–8 (1998)
Jiang, B., Hsu, C.H.: Development of a Fuzzy Decision Model for Manufacturability Evaluation. Journal of Intelligent Manufacturing 14, 169–181 (2003)
Huang, H.Z., Li, Y.H., Xue, L.H.: A Comprehensive Evaluation Model for Assessments of Grinding Machining Quality. Key Engineering Materials 291–292, 157–162 (2005)
Huang, H.Z., Tian, Z.G., Zuo, M.J.: Intelligent Interactive Multiobjective Optimization Method and Its Application to Reliability Optimization. IIE Transactions on Quality and Reliability 37, 983–993 (2005)
Messac, A., Sukam, C.P., Melachrinoudis, E.: Mathematical and Pragmatic Perspectives of Physical Programming. AIAA Journal 39, 885–893 (2001)
Sun, J., Kalenchuk, D.K., Xue, D., Gu, P.: Design Candidate Identification Using Neural Network-Based Fuzzy Reasoning. Robotics and Computer Integrated Manufacturing 16, 383–396 (2000)
Xue, D., Dong, Z.: Coding and Clustering of Design and Manufacturing Features for Concurrent Design. Computers in Industry 34, 139–153 (1997)
Bahrami, A., Lynch, M., Dagli, C.H.: Intelligent Design Retrieval and Packing System: Application of Neural Networks in Design and Manufacturing. International Journal of Production Research 33, 405–426 (1995)
Sun, H.L., Xie, J.Y., Xue, Y.F.: Mechanical Drive Type Decision Model Based on Support Vector Machine. Journal of Shanghai Jiao Tong University 39, 975–978 (2005)
Huang, H.Z., Bo, R.F., Chen, W.: An Integrated Computational Intelligence Approach to Product Concept Generation and Evaluation. Mechanism and Machine Theory 41, 567–583 (2006)
Chen, S.Y.: Engineering Fuzzy Set Theory and Application. National Defence Industry Press, Beijing (1998)
Chen, S.Y., Nie, X.T., Zhu, W.B., Wang, G.L.: A Model of Fuzzy Optimization Neural Networks and Its Application. Advances in Water Science 10, 69–74 (1999)
Chen, S.Y.: Multi-Objective Decision-Making Theory and Application of Neural Network with Fuzzy Optimum Selection. Journal of Dalian University of Technology 37, 693–698 (1997)
Zhang, Y.F., Fuh, J.Y.H.: A Neural Network Approach for Early Cost Estimation of Packaging Products. Computers and Industrial Engineering 34, 433–450 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Gu, Y., He, X. (2007). Neural-Network-Driven Fuzzy Optimum Selection for Mechanism Schemes. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)