ABSTRACT
This chapter focuses on functional-link neural networks. Beginning with the Exclusive-OR problem, it discusses the mathematical essence and the structures of functional-link neural networks. The chapter provides the visualization means of mathematical methods and neural network representations of linear programming and fuzzy linear programming. A single-layer neural network, first studied by M. L. Minsky and S. A. Papert, was named perceptron in 1969. Generally, there are two approaches to solve the nonlinear problem by modifying the architecture of the single-layer perceptron. The first one is to increase number of the hidden layers, and the second one is to add higher order input terms. The chapter shows that neural networks can be used for representing mathematical methods, mathematical forms, or mathematical structures.
