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Soft computing in engineering design: a hybrid dual cross-mapping neural network model

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Abstract

Contemporary design process requires the development of a new computational intelligence or soft computing methodology that involves intelligence integration and hybrid intelligent systems for design, analysis and evaluation, and optimization. This paper first presents a discussion of the need to incorporate “intelligence” into an automated design process and the various constraints that designers face when embarking on industrial design projects. Then, it presents the design problem as optimizing the design output against constraints and the use of soft computing and hybrid intelligent systems techniques. In this paper, a soft-computing-integrated intelligent design framework is developed. A hybrid dual cross-mapping neural network (HDCMNN) model is proposed using the hybrid soft computing technique based on “cross-mapping” between a back-propagation network (BPNN) and a recurrent Hopfield network (HNN) for supporting modeling, analysis and evaluation, and optimization tasks in the design process. The two networks perform different but complementary tasks—the BPNN “decides” if the design problem is a “type 0” (rational) or “type 1” (non-rational) problem, and the output layer weights are then used as the energy function for the HNN. The BPNN is used for representing design patterns, training classification boundaries, and outputting network weight values to the HNN, and then the HNN uses the calculated network weight values to evaluate and modify or re-design the design patterns. The developed system provides a unified soft-computing-integrated intelligent design framework with both symbolic and computational intelligence. The system has self-modifying and self-learning functions. Within the system, only one network training is needed for accomplishing the evaluation, rectification/modification, and optimization tasks in the design process. Finally, two case studies are provided to illustrate and validate the developed model and system.

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Disclaimer and acknowledgement

The bulk of the work reported here by the author was conducted during his tenure at the Nanyang Technological University and Institute of Manufacturing Technology, Singapore. No approval or endorsement by the National Institute of Standards and Technology is intended or implied. The author would like to express his gratitude to the anonymous reviewers of this paper for their insightful comments and suggestions.

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  1. Manufacturing System Integration Division, National Institute of Standards and Technology, Mail Stop 8263, 100 Bureau Drive, Gaithersburg, MD, 20899, USA

    Xuan F. Zha

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  1. Xuan F. Zha
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Correspondence to Xuan F. Zha.

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Zha, X.F. Soft computing in engineering design: a hybrid dual cross-mapping neural network model. Neural Comput & Applic 14, 176–188 (2005). https://doi.org/10.1007/s00521-004-0437-9

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