Neural network approach for inventory control

Artificial neural net models have been studied for many years in the hope of achieving human- like performance in different areas. These nets are composed of many nonlinear computational elements operating in parallel exactly as in biological neural nets. Computational elements or node are connected via weights that are dynamically being changed to improve the overall performance. The neural network studied is a two-layer perceptrons (known also as having three layers), each unit of the first layer is connected to a unit in the hidden layer in turn, is connected to every output unit on the output layer. In most applications, the number of input and output units is known and depends upon the nature of the task and application that are considered. In this paper a neural network model is designed for a two-layer feed-forward perceptron. The neural network has a minimum number of hidden neurons, using the backpropagation training algorithm for a non-complex application in a production plant inventory control. Eventually, designing the neural network architecture means seeking a convergence of this latter within a reasonable amount of time. One of the main issues is to determine the number of hidden neurons and what type of data needs to be entered to get the backpropagation algorithm started.