基于某些人工神经网络的人口预测的研究
发布时间：2018-12-16 15:32
【摘要】：近年来人们不断的研究人口发展的规律，希望能从复杂多变的人口中找到一个规律来预测人口未来的发展，从而制定合理的政策。但人口的增长易受出生率，死亡率等客观因素和人口政策等主观因素的影响，所以一般传统的方法对于人口的预测精度往往达不到所期望的数值。而人工神经网络是一门非线性科学，具有很强的容错性，非线性的映射能力和自适应性，可以使用非线性映射表示人口数量这一非线性系统用以提高模型精度，使它在神经系统方面，模式识别，组合优化，预测等领域有了成功地应用。 本文采用三类人工神经网络：反向传播网络、RBF神经网络、时间序列预测法，研究人口预测，旨在人口预测的特征，综合考虑人口预测的各个指标，从而合理预测人口的增长数量，，为我国的可续发展提供便利。在BP网络中，为了避免网络陷入局部最小点和提高网络的收敛速度，采用动量法与学习速率自适应调整相结合的算法，对于全国人口总人口的预测，采用三层BP神经网络，其中输入层神经元的个数为8，输出层神经元个数为1.而在RBF神经网络中采用的参数是基函数的中心和方差以及权值,在时间序列模型中，采用曲线拟合和参数估计方法(非线性最小二乘法)对网络进行训练。对于影响全国人口总量的各个指标也进行了预测，建立了BP网络，RBF网络和AR模型的预测。 通过选取1990-2008年的人口指标进行预测，预测结果表明，总人口数量预测值与实际值基本吻合，BP预测值与人口总量误差0.0046,0.0011,0.0009,0.0035,0.0000。RBF预测值与人口总量误差为：0.0012,0.00023,0.0062,0.0141，0.0056。AR模型预测值与人口总量误差：0.0031,0.0045，0.0079,0.0002，0.0005。对于其他指标的预测，三种网络的预测值与实际值也非常接近，从而说明神经网络用于人口的预测是可行和有效的，有着良好的前景。
[Abstract]:In recent years, people have been constantly studying the law of population development, hoping to find a law from the complex and changeable population to predict the future development of the population, so as to formulate reasonable policies. However, the population growth is easily affected by objective factors such as birth rate, death rate and population policy. The artificial neural network is a nonlinear science, which has strong fault tolerance, nonlinear mapping ability and adaptability. It can be used to improve the accuracy of the model by using nonlinear mapping to represent the population, which is a nonlinear system. It has been successfully applied in the fields of nervous system, pattern recognition, combinatorial optimization and prediction. In this paper, three kinds of artificial neural networks are used: back propagation network, RBF neural network, time series forecasting method. Thus the reasonable forecast population growth quantity, provides the convenience for our country's sustainable development. In BP network, in order to avoid the network falling into the local minimum point and improve the convergence rate of the network, the momentum method is combined with the adaptive learning rate adjustment algorithm, and the three-layer BP neural network is used to predict the total population of the whole country. The number of neurons in the input layer is 8 and that in the output layer is 1. The parameters used in RBF neural network are the center, variance and weight of the basis function. In the time series model, the methods of curve fitting and parameter estimation (nonlinear least square method) are used to train the network. The BP network, RBF network and AR model are established. By selecting the population index from 1990 to 2008 to forecast, the forecast result shows that the predicted value of the total population quantity basically coincides with the actual value. The forecast value of BP and the total population error are 0.0046C 0.00110.0009U 0.0035N 0.0000.RBF forecast value and total population error: 0.00120.00023N 0.0062n00141N 0.0056.AR model forecast value and population gross error: 0.0031n0045c0.00790.00020.0005. For the prediction of other indexes, the predicted values of the three networks are very close to the actual values, which shows that the neural network is feasible and effective in population prediction, and has a good prospect.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：O212;C921
[Abstract]:In recent years, people have been constantly studying the law of population development, hoping to find a law from the complex and changeable population to predict the future development of the population, so as to formulate reasonable policies. However, the population growth is easily affected by objective factors such as birth rate, death rate and population policy. The artificial neural network is a nonlinear science, which has strong fault tolerance, nonlinear mapping ability and adaptability. It can be used to improve the accuracy of the model by using nonlinear mapping to represent the population, which is a nonlinear system. It has been successfully applied in the fields of nervous system, pattern recognition, combinatorial optimization and prediction. In this paper, three kinds of artificial neural networks are used: back propagation network, RBF neural network, time series forecasting method. Thus the reasonable forecast population growth quantity, provides the convenience for our country's sustainable development. In BP network, in order to avoid the network falling into the local minimum point and improve the convergence rate of the network, the momentum method is combined with the adaptive learning rate adjustment algorithm, and the three-layer BP neural network is used to predict the total population of the whole country. The number of neurons in the input layer is 8 and that in the output layer is 1. The parameters used in RBF neural network are the center, variance and weight of the basis function. In the time series model, the methods of curve fitting and parameter estimation (nonlinear least square method) are used to train the network. The BP network, RBF network and AR model are established. By selecting the population index from 1990 to 2008 to forecast, the forecast result shows that the predicted value of the total population quantity basically coincides with the actual value. The forecast value of BP and the total population error are 0.0046C 0.00110.0009U 0.0035N 0.0000.RBF forecast value and total population error: 0.00120.00023N 0.0062n00141N 0.0056.AR model forecast value and population gross error: 0.0031n0045c0.00790.00020.0005. For the prediction of other indexes, the predicted values of the three networks are very close to the actual values, which shows that the neural network is feasible and effective in population prediction, and has a good prospect.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：O212;C921
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