The Model Base System Development And Critical Problems Research Based On High-Speed Railway And Social Economic Development Interactive Relationship Decision Supporting System

Decision support system is a kind of computer system to assist managers to make decisions, it provides managers with the decision support environment to analyse, model, simulate decision-making and evaluate the results. Model base system is the key, which organizes and stores up the models.What is more,it realizes the interactions between model library and database, through the human-machine interaction interface.Based on high speed railway and social economic development interactive relationship decision support system, the thesis researches at how to establish the model base system with the historical raw data; It is to analyse and evaluate the related models and make a study of some important problems; It designs the related evaluating models and forecast models, which provides the top decision makers with the effective decision support. Content is as follows:1. The paper introduces the way to develop and design the model base system with the SSH framework and MATLAB. The data mining algorithms are simulated by MATLAB and the operation results could be interacted with the database through the Java Bean.2. It describes the methods to complete the organization and storage structure of models using the object-oriented method, including model expression, operation management, input and output management and model operating control.3. It solves this issue that is how to support the combination of the models, sharing and transmission among the model data. Besides, it researches on some critical issues, such as the connection with database and the data exchange.4. It establishes high-speed railway and social economic development interactive relationship analysis and evaluation theories those are consistent with China's national conditions; It describes the method to realize the judge of the related indexes with PCA and DEA methods; It focuses on the forecast and analysis for the indicators using BP neural network based on particle swarm algorithm.It is used to construct the model and verify the stability of the system through the calibration and test.