A hybrid cross-mapping neural network model for computational intelligent design

Article type: Research Article

Authors: Zha, Xuan F.^{; *}

Affiliations: Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075

Correspondence: [*] Current address: National Institute of Standards and Technology, Manufacturing System Integration Division, Mail Stop 8263, 100 Bureau Drive, Gaithersburg, MD 20899, USA. E-mail: zha@nist.gov

Abstract: In order to assist the designer in the design process, it is necessary to develop a new intelligent computational methodology that involves the integration of design, analysis and evaluation, and optimisation. In this paper, a hybrid cross-mapping neural network model (HCMNN) is proposed to support design, analysis and evaluation, and optimisation in the design process, which integrates a back-propagation neural network (BPNN) and a Hopfield neural network (HNN). The BPNN is used for representing design patterns, training classification boundaries, and outputting the net weight values to the HNN, and then the HNN uses the calculated weight values to evaluate and modify or redesign the design patterns. The developed system provides a unified computational intelligent design framework. The system has self-modifying or self-learning functions. Within the system, only one network is needed to train for accomplishing design analysis and evaluation, rectification/modification and optimisation tasks in the design process. Finally, two case studies are provided to verify the proposed model.

DOI: 10.3233/KES-2004-8103

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 8, no. 1, pp. 17-26, 2004