A heuristic neural network initialization scheme for modeling nonlinear functions in engineering mechanics

Jin-Song Pei; Eric C. Mai

doi:10.1117/12.658916

11 April 2006 A heuristic neural network initialization scheme for modeling nonlinear functions in engineering mechanics

Jin-Song Pei, Eric C. Mai

Author Affiliations +

Proceedings Volume 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems; 61741F (2006) https://doi.org/10.1117/12.658916
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, California, United States

Abstract

This paper introduces a heuristic methodology for designing multilayer feedforward neural networks to model the types of nonlinear functions common to many engineering mechanics applications. It is well known that a perfect way to determine the ideal architecture to initialize neural network training has not yet been established. This could be because this challenging issue can only be properly addressed by looking into the features of the function to be approximated and thus might be hard to tackle in a general sense. In this study, the authors do not presume to provide a universal method approximate an arbitrary function, rather the focus is given to modeling nonlinear hysteretic restoring forces, a significant domain function approximation problem. The governing physics and mathematics of nonlinear hysteretic dynamics as well as the strength of the sigmoidal basis function are exploited to determine both an efficient neural network architecture (e.g., the number of hidden nodes) as well as effective initial weight and bias values for those nodes. Training examples are presented to demonstrate and validate the proposed initial design methodology. Comparisons are made between the proposed methodology and the widely used Nguyen-Widrow Initialization. Future work is also identified.

Citation Download Citation

Jin-Song Pei and Eric C. Mai "A heuristic neural network initialization scheme for modeling nonlinear functions in engineering mechanics", Proc. SPIE 6174, Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 61741F (11 April 2006); https://doi.org/10.1117/12.658916

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