兰州大学机构库 >数学与统计学院
基于灰色马尔科夫链的优化模型及其在茶叶产量预测中的应用
Alternative TitleThe Optimized Model Based on Grey Markov Chain and its Application in Prediction of Tea Output
郭书坡
Thesis Advisor王建州
2011-05-19
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword灰色预测 马尔科夫链 粒子群算法 茶叶产量预测
Abstract茶产业是国民经济的重要组成部分,尤其是农业生产的不可或缺的一部分,其意义是很重要的,是不能轻视的。茶产业的顺利开展,对稳定社会生活,顺利开展现代化建设,构建和谐社会等都具有重要意义。对我国茶叶产量进行预测,将有助于有关部门对当前的经济形势进行定量分析,作出科学预测,制定合理可行的政策,从而促进我国茶产业合理、有序的发展。 到目前为止,在粮食产量预测方面,许多学者已经做了大量的研究工作,预测理论取得了很大的发展。但是具体到茶叶,对其产量预测做的研究工作还很少且不充分。从各种研究资料来看,对茶叶产量的预测基本上都是利用单一的预测模型,比如灰色预测模型,许多较好的预测理论在这方面还没有得到很好的应用与开发,本文做的工作就是在茶叶产量预测方面提出了较好的理论研究和实证应用。 本文结合灰色模型和马尔科夫链,建立一种混合的灰色马尔科夫预测模型,对茶叶产量进行预测。灰色理论是一种对含有非确定因素的系统即灰色系统进行预测的一种方法。它是根据现在包括过去的一些已知或非确定的信息,即所谓的灰色信息,建立一个灰色模型(Grey Model,简称GM),,对未知信息进行预测。其中,GM(1,1)是基本的且较为简单的灰色预测模型。传统的GM(1, 1) 灰色预测模型对随机波动性较大的数据序列拟合效果较差,预测精度较低,但可揭示预测数据序列的发展变化的总体趋势。马尔科夫链是根据系统变量的现在状态及其变化趋势,来预测其在下一阶段可能出现的状态, 从而为决策提供依据。马尔科夫过程能够确定状态的转移规律,适合于系统变化随机波动较大的预测问题。由于受到自然条件和社会条件等多种因素的影响,茶叶产量具有很大的随机性和不确定性,它是许多影响因素综合作用的结果。因此,可以把茶叶产量的形成过程看成是灰色动态系统,建立GM(1,1)预测模型。由于茶叶产量的波动性较大,而马尔科夫链预测随机波动规律有一定的优势,运用马尔科夫状态转移矩阵对灰色预测进行修正,建立灰色马尔科夫模型,可以使两种方法取长补短、优势互补,从而提高预测的精度。 为了进一步提高预测模型的准确性,本文提出了利用粒子群算法(PSO)来优化灰色马尔科夫模型的方法,建立一种新的、优化的预测模型,叫粒子群-灰色马尔科夫模型。粒子群算法是一种模拟鸟群寻找食物的生物进化方法,该算法利用种群中个体对信息的共享,个体之间相互协调,相互竞争,自适应的调整搜索方向,具有算法实现简单、需要调整的参数少、与问题的特征信息无关、有较强的全局搜索能力等特点。粒子群-灰色马尔科夫模型利用了粒子群算法的良好特性,进一步提高了预测的精确度。 本文将提出的理论应用到我国茶叶产量的预测中,取得了满意的结果。试验表明,该理论可以应用在我国茶叶产量预测中,为我国茶叶生产规划和决策提供理论依据和参考。
Other AbstractTea industry is an important component of the national economy, especially an integral part of agricultural production. Thus, tea industry is of great Significance, and can not be ignored. The smooth development of tea industry is important for the stability of social life, the modernization as well as the building of a harmonious society. To predict the tea output in China will help the relevant departments quantitatively analyze the current economic situation, scientifically predict, and make the reasonably practicable policy, so as to promote China's tea industry to develop reasonably and orderly. So far, many scholars have done a lot of work on the prediction of grain yield, and the theories of prediction have made significant progress. However, the research work to predict tea output has been done before is little and not enough; there still is lots of work for us to do. From a variety of the research data, we find that the mean to prediction the tea output is only one single predicting model, such as the grey prediction model, and a lot of good prediction model has not been applied and developed. Therefore, the paper tries to present better theoretical research and empirical application on the tea production predicting. In this paper, Markov chain and the gray model are combined to establish a mixed grey Markov prediction model to predict the output of tea. Grey theory is a predicting method for the grey system, including non deterministic factors. Based on the past or present known or undetermined information, which is the so-called gray information, it is principle to establish a gray model (Grey Model, referred to as GM) to predict the unknown information. GM (1,1) is the basically and relatively simple prediction model. Though the traditional GM (1, 1) has a poor effect to fit the random data sequence and predict inaccurately, it can well forecast the general developing and changing trend of the data series. Markov chain can predict the possible state in the next stage based on the present state and the trends of the system variables, and thus provides a basis for the decision-making. Markov process can determine the status of the transfer rule, well be applied to predict some problem with the system’s change having random fluctuations. As a result of natural and social conditions, tea production has a lot of randomness and uncertainty. Therefore, the formation of the tea output can be regarded as a dynamic grey system, we establish GM...
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225170
Collection数学与统计学院
Recommended Citation
GB/T 7714
郭书坡. 基于灰色马尔科夫链的优化模型及其在茶叶产量预测中的应用[D]. 兰州. 兰州大学,2011.
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