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Orponen, P. (2000) An Overview of the Computational Power of Recurrent Neural Networks. In: Hyötniemi, H., Ed., Proceedings of the 9th Finnish AI Conference (STeP 2000—Millennium of AI): AI of Tomorrow, Symposium on Theory (Vol. 3, pp. 89-96), Espoo, 28-30 August 2000, Finnish Artificial Intelligence Society (Suomen Tekoälyseura), Vaasa.

has been cited by the following article:

• TITLE: A Novel Method for Solving Equality and Inequality Constrained Nonlinear Optimization Problems with Artificial Neural Networks

  AUTHORS: Chinelo R. Anokwute, Ben I. Oruh, Olaniyi S. Maliki

  KEYWORDS: ANN, ODE, Network Training, Convergence, MathCAD14

  JOURNAL NAME: American Journal of Operations Research, Vol.16 No.4, July 22, 2026

  ABSTRACT: In this research, we study how artificial neural network can be used in solving both equality and inequality constrained non-linear optimization problems. The procedure involves generating a system of differential equations which must correspond to the given number of intrinsic variables. We proceed to apply this technique to solve some constrained non-linear optimization problems. Using MathCAD14, we were able to generate the corresponding system of differential equations developed using the neural network gradient scheme. We provide the resulting graphical profiles of the solutions and compared it with the analytic solution. Our steps and results indicate that, for the problems tested, the neural network approach can be more straightforward to implement and avoids some of the iterative assumptions of classical methods.