Augmented Integrated Fuzzy Neural Network Learning Model in Structural Engineering

Solving engineering problems is a creative, experiential process. An experienced engineer generally solves a new problem by recalling some similar instances examined before and applying what he learned from the present problem, through adaptation or synthesis. According to such a method, the 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 the IFN model can learn the complicated problems within a reasonable computational time owing to its simplicity. 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 - to facilitate the search for appropriate working parameters in the conventional IFN. The problem of arbitrary trial and error selection of the working parameters is avoided in the augmented IFN learning model.