Building case-based preliminary design systems: A Hopfield network approach

This paper addresses the issue of building a case-based preliminary design system by using Hopfield networks. One limitation of Hopfield networks is that it cannot be trained, i.e. the weights between two neurons must be set in advance. A pattern stored in Hopfield networks cannot be recalled if the pattern is not a local minimum. Two concepts are proposed to deal with this problem. They are the multiple training encoding method and the puppet encoding method. The multiple training encoding method, which guarantees to recall a single stored pattern under appropriate initial conditions of data, is theoretically analyzed, and the minimal number of times for using a pattern for training to guarantee recalling of the pattern among a set of patterns is derived. The puppet encoding method is proved to be able to guarantee recalling of all stored patterns if attaching puppet data to the stored patterns is available.An integrated software PDS (Preliminary Design System), which is developed from two aspects, is described. One is from a case-based expert system—CPDS (Case-based Preliminary Design System), which is based on the algorithm of the Hopfield and developed for uncertain problems in PDS; the other is RPDS (Rule-based Preliminary Design System), which attacks logic or deduced problems in PDS. Based on the results of CPDS, RPDS can search for feasible solution in design model. CPDS is demonstrated to be useful in the domains of preliminary designs of cable-stayed bridges in this paper.

[1]  M S Troitsky.  Cable-stayed bridges: Theory and design , 1977 .

[2]  J. Goodman,et al.  Neural networks for computation: number representations and programming complexity. , 1986, Applied optics.

[3]  Walter Podolny,et al.  Construction and Design of Cable-Stayed Bridges , 1976 .

[4]  R. Narasimhan.  Human intelligence and AI: how close are we to bridging the gap? (open line) , 1990, IEEE Expert.

[5]  Q. Chen.  2+3 Model: Fractal Processes For Knowledge-Based Engineering Design , 1993 .

[6]  George Nagy.  Neural networks-then and now , 1991, IEEE Trans. Neural Networks.

[7]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[8]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[9]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[10]  R. Hecht-Nielsen,et al.  Neurocomputing: picking the human brain , 1988, IEEE Spectrum.

[11]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[12]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[13]  William J. Wolfe,et al.  K-winner networks , 1991, IEEE Trans. Neural Networks.

[14]  Lynn C. Heatley,et al.  Knowledge-based engineering design at Xerox , 1992 .

[15]  Jose B. Cruz,et al.  On multiple training for bidirectional associative memory , 1990, IEEE Trans. Neural Networks.

[16]  J J Hopfield,et al.  Collective computation in neuronlike circuits. , 1987, Scientific American.

[17]  Roger S. Pressman,et al.  Software Engineering: A Practitioner's Approach , 1982 .