论文信息 - A NEURAL NETWORK BASED APPROACH TO MECHANICAL DESIGN OPTIMIZATION

A NEURAL NETWORK BASED APPROACH TO MECHANICAL DESIGN OPTIMIZATION

Abstract A new conceptual framework for solving design optimization problems based on a neural computing paradigm is examined. It is seen that highly interconnected networks of nonlinear analog neurons are extremely effective in computing. These neurons represent an approximation to the biological neurons. The general behavior of an artificial neural network can be readily adapted to solve an optimization problem by appropriately selecting synaptic connections. Results of the computer simulation of a neural network designed to solve four optimization problems are presented to illustrate the computational power of these networks. The optimum solutions obtained using neural networks compare favorably with the optimum solutions obtained using gradient-based search techniques. The results indicate that the efficiency and power of this biological information processing scheme can be effectively utilized to solve other optimization problems with similar efficiency,

Singiresu S Rao | Singiresu S. Rao | A. K. Dhingra | A. Dhingra

[1] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[2] Prabhat Hajela,et al. Neurobiological Computational Models in Structural Analysis and Design , 1990 .

[3] John J. Hopfield,et al. Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[4] Christopher R. Thewalt,et al. Neural Network Approaches in Structural Mechanics Computations , 1989 .

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

[6] Richard P. Lippmann,et al. An introduction to computing with neural nets , 1987 .

[7] S. Grossberg. Neural Networks and Natural Intelligence , 1988 .

[8] A. A. Mullin,et al. Principles of neurodynamics , 1962 .

[9] Lamberto Cesari,et al. Optimization-Theory And Applications , 1983 .

[10] Bernard Widrow,et al. Adaptive switching circuits , 1988 .

[11] D. O. Hebb,et al. The organization of behavior , 1988 .

[12] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.