The Forecast, Based On Intelligent Collaborative Cognitive Stock

Among the study of money market forecasting, Stock Forecasting is a top problem, and there are many excellent algorithms in it Synergetics is an intersecting subject that study system spontaneously comes into being spatial structure, temporal stmcture or functional structure by self-organization. The core concept of Synergetics is Slaving Principle. The rationale of this theory is to transform the system fiom orderless pattern to onler pattern through the cooperation mu competition among slaving powers or order parameters. Furthermore, only 4ren there is slaving power vill the argument above become available. Based on dynamics process of Synergetics, This thesis discuss the application of Synergetic Cognition by applying a top-down approach to neural nets of synergetic computer to build basic Synergetic equations, by which people build the algorithm after discerning the anticipated characteristics. This approach offers us a new type parallel network theory and reveals the profound comparability of pattern recognition and pattern formation, and illuminates further the application of synergetic recognition This thesis applies synergetic computer theory to programming the Intelligent Stock Forecasting system. This system makes use of the trading data of securities business to cany out network training in order to distill prototype pattern vectors ar4 adjoint vectors. These data are imperative for stock price forecasting. hi practice, the result of ow experiment is satisfying. The result indicates that this algorithm is a more efficient means to skeculate trend in future, which achieve forecasting through evolutionary process and associative memory. The experiment illuminates that synergetic cognition has notable effect on not only image recognition but also economical systems recognition.