Study On Quantitative Models, Algorithms And Applications For Regional Medium-and Long-term Development Planning

This dissertation is devoted to some quantitative models, algorithms and applications for regional medium- and long-term development planning. The quantitative methods concern with input-output analysis, artificial neural network and systems optimization methods. The main work of the paper can be summarized as follows:1. Entropy optimization models and algorithms for updating I-O coefficients are considered. Based on the minimum cross-entropy principle of information theory, several entropy optimization models for updating I-O coefficients are established according to prior information. And the relationships between the minimum cross-entropy optimization model and NAIVE, the maximum entropy optimization model, RAS and modified RAS are analyzed, respectively. A dual algorithm for optimization model with an example is presented. Numerical results illustrate that entropy optimization updating method is feasible and effective.2. The asymptotic stability and balanced growth solutions of the dynamic input-output system are studied. Under some natural weak assumptions that do not require the technological coefficients matrix is indecomposable, the fact that the dynamic input-output system is not asymptotically stable and the closed dynamic input-output model exists a balanced growth solution is proved.3. A sensitivity analysis for solutions to dynamic input-output model is performed. The formulations, which compute the changes of national economy sectors' total output during the planning periods when the base year's total output vector and each year's final demand vector vary, are given. Due to the changes of the initial conditions and the exogenous variables, the transfer effects of national economy sectors' total output are revealed.4. Based on artificial neural network technique, a regional economic forecasting system, which has been applied to practical regional medium- and long-term economic forecasting in certain city, is designed. A mixed HS-FR conjugate gradient algorithm is applied to the regional economic forecasting system to train the neural networks effectively. The proposed forecasting system has been applied to forecast main economic indicator of certain city in "the Tenth Five-Year" period, and the forecasting results are adopted by the regional government plan agency to formulate "the Tenth Five-Year" planning.5. Directed at regional medium- and long-term economic forecasting and planning, a conceptual framework of regional economic intelligent forecasting system based on the previous studies is presented, and the application of meta-synthesis in building the forecasting system is analyzed. A regional economic intelligent forecasting models system, in which the core is input-output models, artificial neural networks methods, and optimization techniques, is established.6. A mixed HS-FR conjugate gradient algorithm is proposed. Two convergence theorems without the sufficient descent condition for the mixed HS-FR algorithm are given. The convergence theorem of a class of conjugate gradient algorithms is proven, which extend the main convergence theorem in Gilbert and Noceda (1992).7. For the purpose of analyzing its asymptotic convergence properties the evolution strategy procedures for real-valued function optimization are described. Two convergence theorems, which show that under suitable conditions the evolution strategy asymptotically converges to a global minimum point with probability one, are given. An improved evolutionary programming algorithm for real-valued function optimization is proposed. Numerical results illustrate that the proposed algorithm is feasible and effective.8. According to the demands of making the medium- and long-term highway networks planning in certain province, a goal-programming model for arterial highway network grade structure optimization is established, and an algorithm with example is given.