Augmented IFN Learning Model

Abstract

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. In this work, we present 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.