AUGMENTED IFN LEARNING MODEL

Solving engineering problems is a creative, experiential process. An experienced engineer generally solves a new problem by recalling and reusing some similar instances examined before. According to such a method, the integrated fuzzy neural network (IFN) learning model was developed and implemented as a computational model for problem solving. This model has been applied to design problems involving a complicated steel structure. Computational results indicate that, because of its simplicity, the IFN model can learn the complicated problems within a reasonable computational time. The learning performance of IFN, however, relies heavily on the values of some working parameters, selected on a trial-and-error basis. This work presents an augmented IFN learning model by integrating a conventional IFN learning model with two novel approaches: a correlation analysis in statistics and a self-adjustment in mathematical optimization. This is done to facilitate the search for appropriate working parameters in the conventional IFN. The augmented IFN is compared with the conventional IFN using two steel structure design examples. This comparison reveals a superior learning performance for the augmented IFN learning model. Also, the problem of arbitrary trial-and-error selection of the working parameters is avoided in the augmented IFN learning model.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
• Supplemental Notes:
  • Financial support for this research was provided by the National Science Council of the Republic of China under Contract No. NSC 86-2221-E-009-070.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Hung, S-L
  • Jan, J C
• Publication Date: 2000-1

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: p. 15-22
• Serial:
  • Journal of Computing in Civil Engineering
  • Volume: 14
  • Issue Number: 1
  • Publisher: American Society of Civil Engineers
  • ISSN: 0887-3801

Subject/Index Terms

• TRT Terms: Beams; Correlation analysis; Engineering; Fuzzy algorithms; Machine learning; Mathematical models; Neural networks; Optimization; Problem solving; Steel structures; Structural analysis; Structural design
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; I20: Design and Planning of Transport Infrastructure;

Filing Info

• Accession Number: 00781774
• Record Type: Publication
• Contract Numbers: NSC 86-2221-E-009-070
• Files: TRIS
• Created Date: Jan 22 2000 12:00AM