This work is focused on determninig a nonlinear output error (OE) model, i.e., a dynamic system, by training a two layer neural network with a Levenberg-Marquardt method. Selected as a case study is application of a dynamic model to predict cutting force in machining processes. A model crated by using Artificial Neural Networks (ANN), able to predict the process output, is introduced in order to deal with the characteristics of such an ill-defined process. This model describes the dynamic response of the output before the changes in the process input command (feed rateú and the process parameters (depth of cut). The model provides a sufficiently accurate predition of cutting foce, since the process-dependent specific dynamic properties are adequately reflected.
This paper addresses the problem of stock market data prediction. It discusses the abilities of neural networks to learn and to forecast price quotations as well as proposes a neural approach to the future stock price prediction and detection of high increases or high decreases in stock prices. In order to validate the approach, a large number of experiments were performed on real-life data from the Warsaw Stock Exchange.