The Research On Key Techniques In Intelligent Model Base Based On Function Finding By GEP

With the rapid development of computer technology and application, Decision Support Systems (DSS) were demanded to deal with more complex problems.Intelligent Decision Support Systems (IDSS) have emerged as the times require. Sub-system of Model Base is core of DSS. The traditional methods of constructing Model Base have defects as follows.(1) Users must previously know the types of models in the Model Base. The DSS only calculates the unknown parameters of the models in Model Base in terms of the sample data. Then the decision-maker could predicts in terms of the models. These Model Bases depend on the selection of the type of the model, and do not implement intelligentization for Model Base.(2) The traditional intelligent expert Model Base depends on expert's experiences in specific domain. Such Model Base needs an abundant and comprehensive knowledge base to support and depends on specialist experience of special fields. Consequently, this Model Base does not implement truly intelligentization.(3) If users have not transcendent knowledge for the data to be disposed, they will experientially determine the type of function model with subjectivity and blindness.(4) The traditional methods can not automatically discovery the complex functions and multi-section functions.(5) The traditional Model Bases are weak in expansibility. When the Model Base needs to be expanded, the system must be added new program code.