经济模型的构建与预测方法的研究
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摘要
广东省（主要是珠江三角洲地区）改革开放30年来在经济发展方面取得了较大成绩，目前正面临着环境、产业升级等问题，如何保持经济的持续增长是国家和地方政府要研究和解决的重大问题。本文在广东省发改委“珠三角经济发展战略研究”课题的支持下，针对广东省经济发展及预测规划，建立其经济发展模型，研究用智能控制理论与预测的方法对广东省未来经济发展前景进行预测，为发改委在制定经济发展计划提供科学的决策依据。本文论述了宏观经济的发展历史，分析了古典经济理论、凯恩斯国家干预理论以及市场调节的新古典经济理论的优缺点和时代的局限性，以及经济增长模型的中决定经济增长的关键要素，从控制论的角度对区域经济的发展进行了研究。对国内外宏观经济的建模与预测控制的研究历史和现状进行了分析，针对现代经济规模大、全球化带来的非线性、时变等不确定性，提出了智能化建模与预测的研究设想。结合使用了结构自组织神经网络和强化学习理论和方法，研究了新的经济增长模型，用于广东省的经济预测，为省政府制定珠三角地区未来经济发展规划，预测和控制经济的增长与发展方向提供了手段和参考价值。本文的主要工作如下：
1、针对广东外向型经济成分较大的特点，其经济运行是一个复杂非线性、时变的不确定系统，提出了基于神经网络的自组织增长模型的经济增长预测方法，对引入的神经网络模型预测效果做出评估，并为经济模型的建立和改进提供了具有很高借鉴性的思路。根据该模型，分析了广东省未来的消费水平以及经济发展的走势，对广东未来经济进行预测，为政府调控经济增长提供科学依据。
2、针对基于神经网络的自组织增长模型的经济增长预测方法中的BP人工神经网络存在易于陷入局部极值点等问题，提出一种改进型神经网络算法。在网络训练中采用了附加动量项的学习率自适应的网络学习方法，以减少网络训练时间、次数，提高网络的训练效率；针对宏观经济预测中年份数据偏少的问题，对训练样本集采用链式数据重组的方法进行扩充；针对宏观经济指标数据在我国经济发展背景下呈逐年增长态势和利用历史经济数据训练好的神经网络模型面对新经济数据的预测泛化能力较差的问题，在构造的人工神经网络输入层单元引入经济指标的增长率数据和时间窗口序列数据，提高了所建立的神经网络模型的泛化能力。仿真结果表明提出的算法能够增强神经网络的泛化能力，提高系统模型的预测精度。
3、针对人工神经网络存在收敛速度慢、精度低且容易产生局部收敛的问题，提出了一种基于免疫粒子群优化神经网络的经济预测算法，利用免疫粒子群优化算法对神经网络进行优化，并引入链式数据重组方法扩充样本集，这使得神经网络模型较好的满足了经济系统建模预测的要求，大大加强了网络泛化能力，解决了系统时变时滞问题，与经济预测算法结合在一起，进而提高了系统模型的预测精度。仿真结果表明该算法使得预测误差从原来BP神经元网络的15％下降到改进后的5％。利用该模型调节全社会投资总额和居民人均消费水平，其结果表明提高消费是增强经济发展的内在动力。
4、提出了一种基于改进型的混沌遗传优化SVM经济预测算法。运用LS-SVM模型进行宏观经济模型（以GDP为目标）的建模与预测，同时，利用改进的多尺度混沌遗传优化算法对LS-SVM模型的参数进行优化。进行如GDP等经济指标的精确预测具有巨大的价值。而被预测的经济指标的时间序列函数被作为模型的输入使用，能克服在其他一些非线性模型存在的、GDP影响因素难以全部总结得到的缺陷。仿真实验结果表明提出的算法能使预测精度得到提高，具有更快的收敛速度和更大的泛化能力，平均误差率从BP神经网络的25%降到了2%。
5、针对经济预测与控制中诸如人口的流动、管理的创新以及政府的调控等不确定性主观因素，提出了一种基于动态神经网络的自组织增长模型经济预测算法，该动态优化结构模型算法，引入竞争机制来自动调整神经网络的结构，并在调整结构的同时，对权值的分配调整做了进一步的优化工作，体现出更多的智能，有更好的自组织、自适应和自学习能力。既解决了固定网络模型结构引起的较大逼近误差，又改善控制预测性能。用于对广东省的经济预测的仿真的结果表明，动态优化结构的神经网络控制算法对经济的预测达到了较高的预测效果。
伴随着市场经济发展的逐步深人，宏观经济的波动特征对在我国的经济运行中表现得更加明显。本文针对珠三角地区宏观经济的控制与预测问题，在神经网络这一智能算法的基础上，对建模和预测控制方法提出了一些改进思路，对经济建模与预测控制进行了有意义的研究和探索，具有较强的理论价值和实际意义。
The reform and opening in Guangdong Province, especially in the Pearl River Delta region, has made great achievements in economic development in the past of thirty years. The environmental, industrial upgrading and other issues occur with the development of economy. How to maintain sustained economic growth is one of most important issues to be studied and resolved for the state and local governments. Under the great support of the project "the Pearl River Delta Economic Development Strategy" from Guangdong Province Development and Reform Commission, the thesis focuses on the establishment of the economic development model for the forecast planning in Guangdong and, and applies the intelligent control and prediction techniques to predict the future economic development of Guangdong Province. The results might provide a scientific basis for decision making for the development and Reform Commission in the formulating economic development plans. This thesis firstly discusses the history of the macroeconomic development, and then systematically analyzes the classical economic theory, the key elements of the Keynes's state intervention theory, and the market regulation neo-classical economic theory. After analyzing the advantages, disadvantages and limitations of the existing methods, and the key elements of the economic growth model, we study the regional economic development from the point view of the control theory. After sufficiently studying the history and current status of the modeling of the domestic and international macroeconomic and predictive control, we present intelligent modeling and forecasting method for the modern economic nonlinear model with large-scale, globalization, time-varying uncertainty. Combining the structure of self-organizing neural networks with the reinforcement learning method, a novel economic growth model is presented to forecast the economy of Guangdong Province and to provide reference value for Guangdong Province in the future economic development planning for the Pearl River Delta region. The main results of the thesis are listed as follows:
(1) Considering the large export-oriented economic components in Guangdong, it is fact that the economic operation is a nonlinear complex and time-varying uncertain system. Based on a self-organized growth neural network model, we present an economic growth prediction method. We also assess the predict effect of neural network model. The method could provide a helpful idea for the establishment and the improvement of the economic model. Based the proposed model, we predict the future level of consumption and the trend of economic development in Guangdong Province. The prediction provides a scientific basis for government regulation on the economic growth.
(2) An improved network algorithm is presented for to overcome the problem of local extreme points for the BP artificial neural growth model in the economic growth prediction. The adaptive learning rate of the additional momentum in the neural network training is used to reduce network training time and number, and to improve the efficiency of the network training. Using the chain data reorganization method, the training sample set is expanded to solve the problem of few year data in the macroeconomic forecast. Usually, the trained neural network model has poor generalization ability in the economic data predict for the growth macro-economic indicators in the context of China's economic development. We introduce the growth rate data of the economic indicators and the series data of the time window to improve the generalization ability of the proposed neural network model. The simulation results show that the proposed algorithm could enhance the generalization ability of neural network and improve the prediction accuracy of the system model.
(3) An improved immune particle swarm optimization neural network algorithm is presented to overcome the problem of slow convergence, low accuracy and local convergence. An immune particle swarm optimization algorithm is used to optimize the neural network. A linked data reorganization method is introduced to expand the sample set. This method could satisfy the requirements of the economic system modeling, and greatly strengthen the network generalization capabilities. By combining with the economic prediction algorithm, the method improves the prediction accuracy of the system model. The simulation results show the prediction error is dropped from15%obtained by the original BP neural network to5%. The model is applied to adjust the total social investment and per capita consumption level. The simulation results show that the consumption increasing is the intrinsic motivation of the economic development.
(4) We propose an improved optimization algorithm for the SVM economic forecasting. LS-SVM model is used to mode and predict the GDP in the macroeconomic model. The LS-SVM model parameters is optimized by using multi-scale chaotic genetic optimization. The precise forecast for the GDP and other economic indicators is great meaningful for the economic development planning. A time function of the economic forecast indicators is introduced as the model input. Simulation results show that the proposed algorithm could improve the prediction accuracy, with fast convergence and great generalization ability. The average error rate is small than2%while the error rate yielded by the BP neural network is25%.
(5) Considering the uncertainties which may raise from the population flow, management innovation, and government regulation, we develop a dynamic optimization structure model to forecast the economic development of Guangdong Province. This model introduces a competitive mechanism to automatically adjust the structure of the neural network and further optimizes the weight allocation adjustments. This method can overcomes the larger approximation errors in the model structure of the fixed networks and improves the prediction control performance. Simulation on the economic forecasts for Guangdong Province is carried out in the theise. The simulation results show that the proposed dynamic optimization algorithm with neural network structure could achieve a satisfying prediction in the economic forecast.
Finally, with the great development of the market economy, the volatility characteristics of effects China's economic operation. We studies the problm of macroeconomic control and prediction of the Pearl River Delta region. Based on the neural network intelligent algorithm, we propose improved control performance and forecasting methods. This methods is a meaningful attempt for the predictive control of the economy and have strong theoretical values and practical significance.
1、针对广东外向型经济成分较大的特点，其经济运行是一个复杂非线性、时变的不确定系统，提出了基于神经网络的自组织增长模型的经济增长预测方法，对引入的神经网络模型预测效果做出评估，并为经济模型的建立和改进提供了具有很高借鉴性的思路。根据该模型，分析了广东省未来的消费水平以及经济发展的走势，对广东未来经济进行预测，为政府调控经济增长提供科学依据。
2、针对基于神经网络的自组织增长模型的经济增长预测方法中的BP人工神经网络存在易于陷入局部极值点等问题，提出一种改进型神经网络算法。在网络训练中采用了附加动量项的学习率自适应的网络学习方法，以减少网络训练时间、次数，提高网络的训练效率；针对宏观经济预测中年份数据偏少的问题，对训练样本集采用链式数据重组的方法进行扩充；针对宏观经济指标数据在我国经济发展背景下呈逐年增长态势和利用历史经济数据训练好的神经网络模型面对新经济数据的预测泛化能力较差的问题，在构造的人工神经网络输入层单元引入经济指标的增长率数据和时间窗口序列数据，提高了所建立的神经网络模型的泛化能力。仿真结果表明提出的算法能够增强神经网络的泛化能力，提高系统模型的预测精度。
3、针对人工神经网络存在收敛速度慢、精度低且容易产生局部收敛的问题，提出了一种基于免疫粒子群优化神经网络的经济预测算法，利用免疫粒子群优化算法对神经网络进行优化，并引入链式数据重组方法扩充样本集，这使得神经网络模型较好的满足了经济系统建模预测的要求，大大加强了网络泛化能力，解决了系统时变时滞问题，与经济预测算法结合在一起，进而提高了系统模型的预测精度。仿真结果表明该算法使得预测误差从原来BP神经元网络的15％下降到改进后的5％。利用该模型调节全社会投资总额和居民人均消费水平，其结果表明提高消费是增强经济发展的内在动力。
4、提出了一种基于改进型的混沌遗传优化SVM经济预测算法。运用LS-SVM模型进行宏观经济模型（以GDP为目标）的建模与预测，同时，利用改进的多尺度混沌遗传优化算法对LS-SVM模型的参数进行优化。进行如GDP等经济指标的精确预测具有巨大的价值。而被预测的经济指标的时间序列函数被作为模型的输入使用，能克服在其他一些非线性模型存在的、GDP影响因素难以全部总结得到的缺陷。仿真实验结果表明提出的算法能使预测精度得到提高，具有更快的收敛速度和更大的泛化能力，平均误差率从BP神经网络的25%降到了2%。
5、针对经济预测与控制中诸如人口的流动、管理的创新以及政府的调控等不确定性主观因素，提出了一种基于动态神经网络的自组织增长模型经济预测算法，该动态优化结构模型算法，引入竞争机制来自动调整神经网络的结构，并在调整结构的同时，对权值的分配调整做了进一步的优化工作，体现出更多的智能，有更好的自组织、自适应和自学习能力。既解决了固定网络模型结构引起的较大逼近误差，又改善控制预测性能。用于对广东省的经济预测的仿真的结果表明，动态优化结构的神经网络控制算法对经济的预测达到了较高的预测效果。
伴随着市场经济发展的逐步深人，宏观经济的波动特征对在我国的经济运行中表现得更加明显。本文针对珠三角地区宏观经济的控制与预测问题，在神经网络这一智能算法的基础上，对建模和预测控制方法提出了一些改进思路，对经济建模与预测控制进行了有意义的研究和探索，具有较强的理论价值和实际意义。
The reform and opening in Guangdong Province, especially in the Pearl River Delta region, has made great achievements in economic development in the past of thirty years. The environmental, industrial upgrading and other issues occur with the development of economy. How to maintain sustained economic growth is one of most important issues to be studied and resolved for the state and local governments. Under the great support of the project "the Pearl River Delta Economic Development Strategy" from Guangdong Province Development and Reform Commission, the thesis focuses on the establishment of the economic development model for the forecast planning in Guangdong and, and applies the intelligent control and prediction techniques to predict the future economic development of Guangdong Province. The results might provide a scientific basis for decision making for the development and Reform Commission in the formulating economic development plans. This thesis firstly discusses the history of the macroeconomic development, and then systematically analyzes the classical economic theory, the key elements of the Keynes's state intervention theory, and the market regulation neo-classical economic theory. After analyzing the advantages, disadvantages and limitations of the existing methods, and the key elements of the economic growth model, we study the regional economic development from the point view of the control theory. After sufficiently studying the history and current status of the modeling of the domestic and international macroeconomic and predictive control, we present intelligent modeling and forecasting method for the modern economic nonlinear model with large-scale, globalization, time-varying uncertainty. Combining the structure of self-organizing neural networks with the reinforcement learning method, a novel economic growth model is presented to forecast the economy of Guangdong Province and to provide reference value for Guangdong Province in the future economic development planning for the Pearl River Delta region. The main results of the thesis are listed as follows:
(1) Considering the large export-oriented economic components in Guangdong, it is fact that the economic operation is a nonlinear complex and time-varying uncertain system. Based on a self-organized growth neural network model, we present an economic growth prediction method. We also assess the predict effect of neural network model. The method could provide a helpful idea for the establishment and the improvement of the economic model. Based the proposed model, we predict the future level of consumption and the trend of economic development in Guangdong Province. The prediction provides a scientific basis for government regulation on the economic growth.
(2) An improved network algorithm is presented for to overcome the problem of local extreme points for the BP artificial neural growth model in the economic growth prediction. The adaptive learning rate of the additional momentum in the neural network training is used to reduce network training time and number, and to improve the efficiency of the network training. Using the chain data reorganization method, the training sample set is expanded to solve the problem of few year data in the macroeconomic forecast. Usually, the trained neural network model has poor generalization ability in the economic data predict for the growth macro-economic indicators in the context of China's economic development. We introduce the growth rate data of the economic indicators and the series data of the time window to improve the generalization ability of the proposed neural network model. The simulation results show that the proposed algorithm could enhance the generalization ability of neural network and improve the prediction accuracy of the system model.
(3) An improved immune particle swarm optimization neural network algorithm is presented to overcome the problem of slow convergence, low accuracy and local convergence. An immune particle swarm optimization algorithm is used to optimize the neural network. A linked data reorganization method is introduced to expand the sample set. This method could satisfy the requirements of the economic system modeling, and greatly strengthen the network generalization capabilities. By combining with the economic prediction algorithm, the method improves the prediction accuracy of the system model. The simulation results show the prediction error is dropped from15%obtained by the original BP neural network to5%. The model is applied to adjust the total social investment and per capita consumption level. The simulation results show that the consumption increasing is the intrinsic motivation of the economic development.
(4) We propose an improved optimization algorithm for the SVM economic forecasting. LS-SVM model is used to mode and predict the GDP in the macroeconomic model. The LS-SVM model parameters is optimized by using multi-scale chaotic genetic optimization. The precise forecast for the GDP and other economic indicators is great meaningful for the economic development planning. A time function of the economic forecast indicators is introduced as the model input. Simulation results show that the proposed algorithm could improve the prediction accuracy, with fast convergence and great generalization ability. The average error rate is small than2%while the error rate yielded by the BP neural network is25%.
(5) Considering the uncertainties which may raise from the population flow, management innovation, and government regulation, we develop a dynamic optimization structure model to forecast the economic development of Guangdong Province. This model introduces a competitive mechanism to automatically adjust the structure of the neural network and further optimizes the weight allocation adjustments. This method can overcomes the larger approximation errors in the model structure of the fixed networks and improves the prediction control performance. Simulation on the economic forecasts for Guangdong Province is carried out in the theise. The simulation results show that the proposed dynamic optimization algorithm with neural network structure could achieve a satisfying prediction in the economic forecast.
Finally, with the great development of the market economy, the volatility characteristics of effects China's economic operation. We studies the problm of macroeconomic control and prediction of the Pearl River Delta region. Based on the neural network intelligent algorithm, we propose improved control performance and forecasting methods. This methods is a meaningful attempt for the predictive control of the economy and have strong theoretical values and practical significance.
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