Neural network learning and expert systems
Neural network learning and expert systems
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Guest Editors' Introduction: Neurocomputing - Motivation, Models, and Hybridization
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Intelligent Systems for Business: Expert Systems with Neural Networks
Intelligent Systems for Business: Expert Systems with Neural Networks
Decision Support and Expert Systems: Management Support Systems
Decision Support and Expert Systems: Management Support Systems
Information Technology for Management: Improving Quality and Productivity
Information Technology for Management: Improving Quality and Productivity
Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance
Stock Market Prediction with Backpropagation Networks
IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
MCBE'08 Proceedings of the 9th WSEAS International Conference on Mathematics & Computers In Business and Economics
MCBE'08 Proceedings of the 9th WSEAS International Conference on Mathematics & Computers In Business and Economics
The role of predictability of financial series in emerging market applications
WSEAS Transactions on Mathematics
New ideas on the artificial intelligence support in military applications
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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In the aftermath of the actual financial crisis, all the MoDs are preparing severe budget cuts in response to the decline in their national income. The costs of transaction dedicated to military procurement are still very high and the efficiency of using this funds in military capability improvements is altered. A new framework based on hybrid trading system for defence spending aplications (HTS-D) that incorporates different ingredients like the chaos theory (CHT), non-linear statistical models (NLSM) and soft computing (SC) methods (Artificial Neural Networks- ANN, Fuzzy Logic- FL and Genetic Algorithms- GA) is proposed. The HTS-D framework can be defined in three phases: time series selection by using chaos theory to identify non-random series; forecasting the time series by using ANNs and NLSM; implementation of a rule-based HTS that may incorporate GAs.