COUNTERPROPAGATION NEURAL NETWORKS IN STRUCTURAL ENGINEERING

Neural network computing has recently been applied to structural engineering problems. Most of the published research is based on a back-propagation neural network (BPN), primarily due to its simplicity. The back-propagation algorithm, however, has a slow rate of learning and is therefore impractical for learning of complicated problems requiring large networks. In this paper, we present application of counterpropagation neural network (CPN) with competition and interpolation layers in structural analysis and design. To circumvent the arbitrary trial-and-error selection of the learning coefficients encountered in the counterpropagation algorithm, a simple formula is proposed as a function of the iteration number and excellent convergence is reported. The CPN is compared with the BPN using two structural engineering examples reported in recent literature. We found superior convergence property and a substantial decrease in the central processing unit (CPU) time for the CPN. In addition, CPN was applied to ...

[1]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[2]  Hojjat Adeli,et al.  Supercomputing in Engineering Analysis , 1992 .

[3]  Hojjat Adeli,et al.  A model of perceptron learning with a hidden layer for engineering design , 1991, Neurocomputing.

[4]  Stephen Grossberg,et al.  Studies of mind and brain , 1982 .

[5]  Hojjat Adeli,et al.  Parallel backpropagation learning algorithms on CRAY Y-MP8/864 supercomputer , 1993, Neurocomputing.

[6]  A. R. Hurson,et al.  Automated Knowledge Acquisition in a Neural Network–Based Decision Support System for Incomplete Database Systems , 1994 .

[7]  Hojjat Adeli,et al.  Parallel Processing in Computational Mechanics , 1992 .

[8]  R. Daniel VanLuchene,et al.  INTEGRATED ASSESSMENT OF SEISMIC DAMAGE IN STRUCTURES , 1994 .

[9]  H. Adeli,et al.  OBJECT-ORIENTED FINITE ELEMENT ANALYSIS USING EER MODEL , 1993 .

[10]  R. D. Vanluchene,et al.  Neural Networks in Structural Engineering , 1990 .

[11]  Hojjat Adeli,et al.  A Concurrent Adaptive Conjugate Gradient Learning Algorithm On Mimd Shared-Memory Machines , 1993, Int. J. High Perform. Comput. Appl..

[12]  Ricardo O. Foschi,et al.  Object-oriented finite element analysis , 1990 .

[13]  Tarek Hegazy,et al.  Developing Practical Neural Network Applications Using Back‐Propagation , 1994 .

[14]  Robert Hecht-Nielsen,et al.  Applications of counterpropagation networks , 1988, Neural Networks.

[15]  Hojjat Adeli,et al.  Object-oriented backpropagation and its application to structural design , 1994, Neurocomputing.

[16]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[17]  Prabhat Hajela,et al.  Neurobiological computational models in structural analysis and design , 1991 .

[18]  S. Masri,et al.  Identification of Nonlinear Dynamic Systems Using Neural Networks , 1993 .

[19]  Hojjat Adeli,et al.  Fuzzy Neural Network Learning Model for Image Recognition , 1993 .

[20]  Hojjat Adeli,et al.  A parallel genetic/neural network learning algorithm for MIMD shared memory machines , 1994, IEEE Trans. Neural Networks.

[21]  C. John Yoon,et al.  Neural Network Approaches to Aid Simple Truss Design Problems , 1994 .

[22]  George C. Lee,et al.  A Structural Damage Neural Network Monitoring System , 1994 .

[23]  H. Adeli,et al.  Microtasking, Macrotasking, and Autotasking for Structural Optimization , 1994 .

[24]  Hojjat Adeli,et al.  An adaptive conjugate gradient learning algorithm for efficient training of neural networks , 1994 .

[25]  John S. Gero,et al.  Effect of Representation on the Performance of Neural Networks in Structural Engineering Applications , 1994 .

[26]  Nicholas S. Trahair,et al.  Buckling of monosymmetric I beams under moment gradient , 1986 .

[27]  D. Woods,et al.  Back and counter propagation aberrations , 1988, IEEE 1988 International Conference on Neural Networks.

[28]  John Messner,et al.  StructNet: A Neural Network for Structural System Selection , 1994 .