Practical constraints pertinent to the design of neural networks

Said Abdallah; Rufus H. Cofer

doi:10.1117/12.139946

20 August 1992 Practical constraints pertinent to the design of neural networks

Said Abdallah, Rufus H. Cofer

Proceedings Volume 1706, Adaptive and Learning Systems; (1992) https://doi.org/10.1117/12.139946
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

in designing a feedforward neural network for numerical computation using the backpropagation algorithm it is essential to know that the resulting network has a practical global minimum, meaning that convergence to a stationary solution can be achieved in reasonable time and using a network of reasonable size. This is in contrast to theoretical results indicating that any square-integrable (L2) function can be computed assuming that an unlimited number of neurons are available. A class of problems is discussed that does not fit into this category. Although these problems are conceptually simple, it is shown that in practice convergence to a stationary solution can only be approximate and very costly. Computer simulation results are shown, and concepts are presented that can improve the performance by a careful redesign of the problem.

Citation Download Citation

Said Abdallah and Rufus H. Cofer "Practical constraints pertinent to the design of neural networks", Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); https://doi.org/10.1117/12.139946

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