Mining Based ID3 Maximum Multifactor Dimensionality Posteriori Method for Efficient Survival on Financial Time Series Detection

The forecast exchange rate has become more and more attention, especially because of the important financial issues, inherent difficulties and practical applications; many attempts to improve the nonlinear model to obtain accurate predictions. Performance-based mining ID3 maximum number of dimensions of the multi-element method close. Among them, the neural network model is based on data mining to encourage results. This gift is one step of their performance. Several methods, radiation-based function, dynamic neural networks and fuzzy systems, discussion and recommendations, including a multi-element dimensions progeny. It improves neural networks and fuzzy models used to predict the exchange rate and a multi-step ahead forecast. Throughout the investigation process, it will be evaluated using the actual value per day of the exchange rate and the British pound in U.S. dollars

FOREX Prediction Using An Artificial Intelligence System

The purpose of this study is to examine the use and applicability of an artificial intelligence system in predicting changes in foreign currency exchange rates. There are algorithms available for that purpose and this study compares several of these algorithms for efficiency and accuracy. This comparison was carried out through the use of the Metlab computer software program. The multi-layer back-propagation neural network was chosen for this research. We use feed-forward topologies, supervised learning and back-propagation learning algorithms on the network. This program allows for training neural networks, thereby producing predictions of future foreign currency exchange rates. This paper builds a model for pattern recognition of foreign currency exchange rate trends. The methodology used in this paper was successful in that neural networks were successfully trained and predictions of future foreign currency exchange rates were produced. A total of eleven algorithms and different exchange rates were compared and tested through the neural network training procedure. The Levenberg-Marquardt algorithm is best suited to deal with a function approximation problem where the network has up to several hundred weights, and the approximation must be very accurate. Over all, of the algorithms considered, the Levenberg-Marquardt algorithm appears to be the most appropriate for the purposes of this paper.Computer Science Departmen

An adaptive hierarchical fuzzy logic system for modelling and prediction of financial systems

In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to model and predict interest rates. A Feed Forward Fuzzy Logic system using fuzzy logic and genetic algorithms is developed to predict interest rates for three months periods. A back-propagation Hierarchical Neural Network system is further developed to predict interest rates for three months, six months and one year periods. These two systems are then compared with the Hierarchical Fuzzy Logic system results and conclusions on their accuracy of prediction are compared

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation