Feedforward neural nets and one-dimensional representation

Laurence C. W. Dixon; David Mills

doi:10.1117/12.140100

1 July 1992 Feedforward neural nets and one-dimensional representation

Laurence C. W. Dixon, David Mills

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140100
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Feedforward nets can be trained to represent any continuous function, and training is equivalent to solving a nonlinear optimization problem. Unfortunately, it frequently leads to an error function with a Hessian matrix that is effectively singular at the solution. Traditional quadratic based optimization algorithms do not perform superlinearly on functions with a singular Hessian, but results on univariate functions show that even so they are more efficient and reliable than backpropagation. A feedforward net is used to represent a superposition of its own sigmoid activation function. The results identify some conditions for which the Hessian of the error function is effectively singular.

Citation Download Citation

Laurence C. W. Dixon and David Mills "Feedforward neural nets and one-dimensional representation", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140100

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