A neural network representation of linear programming

This paper demonstrates the flexibility of neural networks for modeling and solving diverse mathematical problems. Advantages of using neural networks to solve problems include clear visualization, powerful computation and that they are easy to be made into hardware. In this paper, the well-known exclusive OR problem is first introduced. Then, two examples are discussed in order to show how to use neural networks to represent different problems. One problem is Taylor series expansion and the other is Weierstrass's first approximation theorem. The neural representation of linear programming and the neural representation of fuzzy linear programming are also discussed.