This paper deals with neural-predictive algorithm for some nonlinear
processes in the industry. Neural model predictive control (NMPC) uses artificial neural networks (ANN) for modeling the process and for configuration of the optimizer. The optimizer sets up on-line controller parameters by predicting next control action signals. Depending on the number of prediction steps, the optimizer can predict the process behavior in the future. Therefore this type of predictive control is very useful for the control of the highly nonlinear processes, which are known for their various behaviors. One practical example is the isothermal polymerization reactor where the NMPC Controls the oiitput variable very robustly. Finally, this control method is compared with the linear PID controller designed to solve this problém using a genetic algorithm.
Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).