A High Precision Global Prediction Approach Based
ai在农业中的应用英语作文
ai在农业中的应用英语作文The Application of AI in Agriculture.Artificial intelligence (AI) has revolutionized various industries, and agriculture is no exception. Theintegration of AI in agriculture has brought about significant advancements, enhancing production efficiency, optimizing resource utilization, and ensuring sustainable food production. In this essay, we delve into the various applications of AI in agriculture, discussing how it has transformed this vital sector.Precision Farming.Precision farming, also known as precision agriculture, is one of the most significant applications of AI in agriculture. This technology allows farmers to monitor and manage their fields with unprecedented precision. Through the use of sensors, satellites, drones, and other AI-powered tools, farmers can gather detailed data about theirfields, including soil conditions, crop health, and weather patterns. This data can then be analyzed by AI algorithms to provide farmers with insights into the optimal time for planting, watering, and fertilizing.Precision farming not only improves crop yields but also reduces the environmental impact of agriculture. By precisely applying inputs like water and fertilizer, farmers can reduce waste and minimize soil erosion and pollution. Furthermore, AI-based prediction models can forecast potential problems, such as disease outbreaks or pests, enabling farmers to take proactive measures to mitigate these issues.Automated Equipment.AI has also revolutionized agricultural equipment, leading to the development of automated tractors, robots, and drones. These automated machines can perform tasks like planting, weeding, harvesting, and monitoring, greatly reducing the manual labor required in agriculture. Automated equipment not only increases productionefficiency but also ensures consistent quality and reduces human error.Moreover, AI-powered robots and drones can operate in environments that are too dangerous or inaccessible for humans, such as dense forests or fields infected with harmful pesticides. This capability not only enhances safety but also extends the operational hours, allowing for round-the-clock monitoring and maintenance.Crop Disease Detection.Crop diseases can cause significant losses to farmers, affecting both yield and quality. AI-based image recognition systems can help farmers detect diseases early, enabling them to take prompt action and minimize losses. These systems analyze high-resolution images of crops, identifying subtle changes in color, shape, or texture that indicate the presence of a disease.By combining AI with other technologies like sensors and weather data, farmers can gain a comprehensiveunderstanding of the causes and spread of diseases. This information can then be used to develop targeted control measures, such as applying specific pesticides or adjusting irrigation schedules.Sustainable Agriculture.Sustainability is a crucial aspect of modern agriculture, and AI can play a pivotal role in achieving it. AI-based models can help farmers optimize their resource use, reducing waste and minimizing the environmental impact of agriculture. For instance, AI algorithms can analyzesoil and weather data to recommend the optimal amount of fertilizer and water required for crop growth.Additionally, AI can help farmers adopt sustainable farming practices, such as crop rotation and integratedpest management. These practices not only improve soilhealth and biodiversity but also reduce the need for chemical pesticides, ensuring safer and moreenvironmentally friendly food production.Challenges and Future Prospects.While the applications of AI in agriculture are promising, several challenges need to be addressed. One of the main challenges is the high cost of AI-based systems, which may be unaffordable for small-scale farmers. Additionally, the widespread adoption of AI in agriculture requires a skilled workforce, and there is a need for more training and education programs in this area.Despite these challenges, the future of AI in agriculture looks bright. With advances in technology and the increasing availability of data, AI-based systems will become more accurate and efficient, further revolutionizing agriculture. Moreover, as more farmers adopt AI-based practices, we can expect to see significant improvements in food production, sustainability, and economic growth.In conclusion, AI has transformed agriculture, bringing about unprecedented advancements in precision farming, automated equipment, crop disease detection, and sustainable agriculture. As we move forward, it is crucialto address the challenges and ensure that AI-based systems are accessible and affordable for all farmers. By doing so, we can ensure a sustainable and prosperous future for agriculture, feeding the world's growing population while protecting our environment.。
航海英语阅读理解题集
航海英语阅读理解题集Unit 01Passage 1 Admiralty TotalTideAdmiralty TotalTide (DP550) is a PC-based （基于个人电脑）tidal prediction program （预测程序）which uses the same prediction algorithms （运算法则）and Harmonic Constants （谐和常数）as the Admiralty TotalTide, and has been designed to meet SOLAS carriage requirements（运输条款）.Tidal heights for both Standard and Secondary Ports are displayed in graphical and tabular form以图表形式. Tidal Stream rates 潮流速率are presented on a chart-based diagram表示在一张航用海图上的简图.TotalTide permits the mariner to select and simultaneously 有选择地或同步calculate tidal heights for multiple ports 多个港口for up to seven days最多达到7天. Output from the system 从该系统输出的形式also includes periods of daylight 白昼and nautical twilight航海的黄昏、黎明, moon phases and a springs and neaps （大、小潮）indicator. Underkeel and overhead clearance 富裕水深和高处间隙can be displayed in a graphic form 以图表形式to aid passage planning帮助航路设计.TotalTide is supplied in the form of a single CD 光盘which contains the calculation program and the seven geographic Area Data Sets 7个地区的汇总数据资料(ADS) providing global coverage. A permit system then provides access to the areas required. Annual updates 年度的更新资料for TotalTide are available from Admiralty Chart Agents, and are recommended.1. The Admiralty TotalTide (Dp550) is .A．an article abstracted from SOLAS B．a Book known as Admiralty TotalTideC．an Admiralty Chart Agent D．a PC-based tidal prediction program2. is not an item contained in the output of the Admiralty TotalTide.A．periods of daylight and nautical twilight B．moon phasesC．an indicator of springs and neaps D．the seven geographic Area Data Sets3. Underkeel and overhead clearances are used to .A．calculate tidal heights for multiple ports B．select recommended sailing directionsC．display in graphical and tabular form of Tidal Stream rates D．aid passage planning4. It is inferred that the prediction algorithms are used for .A．displaying in graphical and tabular form of tidal heightsB．updating of the Admiralty TotalTideC．an calculation of the program and the seven geographic Area Data SetsD．the determination of tides and currents for certain area concenedPassage 2 Ocean Passages for the World世界大洋航路For the mariner planning an ocean passage为了让航海人员设计一条远洋航路, Ocean Passages for the World（NP136）provides a selection of commonly used routes 从常用航线中挑选了一部分with their distances between principal ports and important positions. It contains details of weather, currents and ice hazards appropriate to the routes, and so links the volumes of Sailing Directions并与航路指南相衔接. It also gives other useful information on Load Line 载重线Rules, Weather Routeing气象航路.The volume is in two parts: Part I gives routes for powered vessels机动船; Parts II gives routes used in the past by sailing ships帆船, edited from former editions to bring names up-to-date 根据编辑，前者所出现的名称是最新的, and with certain notes added并附加了某些注意事项. The book is updated by本书由……更新Section IV of Admiralty Notices to Mariners, Weekly Editions, and periodically by supplements由补篇周期性地更新.5. is not contained in Ocean Passages for the World（NP136）.A．Details of weather B．Currents appropriate to the routesC．Ice hazards appropriate to the routes D．Tonnage measurement6. Ocean Passages for the World（NP136）is updated by .A．Weekly NW B．Weather RouteingC．circulars from IMO D．certain notes7. Part I of Ocean Passages for the World gives .A．routes used in the past by sailing ships B．routes for powered vesselsC．supplements D．useful information on Load Line Rules, Weather Routeing, etc.8. Contained in the Ocean Passages for the World (NP136) is also the information linking the volumes of .A．Admiralty Notices to Mariners B．Sailing DirectionsC．Load Line Rules D．Weather RouteingPassage 3 The Nautical Almanac航海天文历, Star Finder Identifier索星卡和星球仪The Nautical Almanac tabulates all date for the year required for the practice of astronomical navigation at sea.为海上天文航海实践的需要，航海天文历均以表格形式按一年的期限给出所的的数据。
基于GA-BP_神经网络晶粒尺寸预测模型的轮端轮毂锻造工艺优化
精 密 成 形 工 程第16卷 第3期 44JOURNAL OF NETSHAPE FORMING ENGINEERING 2024年3月收稿日期：2024-01-15 Received ：2024-01-15基金项目：国家重点研发计划（2022YFB3706903）；国家自然科学基金（52090043）Fund ：National Key R&D Program of China (2022YFB3706903); The National Natural Science Foundation of China (52090043) 引文格式：孔德瑜, 晏洋, 张浩, 等. 基于GA-BP 神经网络晶粒尺寸预测模型的轮端轮毂锻造工艺优化[J]. 精密成形工程, 2024, 16(3): 44-51.KONG Deyu, YAN Yang, ZHANG Hao, et al. Optimization of Wheel End Hub Forging Process Based on GA-BP Neural Network Grain Size Prediction Model[J]. Journal of Netshape Forming Engineering, 2024, 16(3): 44-51. *通信作者（Corresponding author ）基于GA-BP 神经网络晶粒尺寸预测模型的轮端轮毂锻造工艺优化孔德瑜1，晏洋2，张浩1，邓磊1*，王新云1，龚攀1，张茂1（1.华中科技大学 材料成形与模具技术全国重点实验室，武汉 430074；2.湖北三环锻造有限公司，湖北 襄阳 441700）摘要：目的 针对6082铝合金轮端轮毂在热处理过程中出现的粗晶问题，利用基于遗传算法优化的BP 神经网络晶粒尺寸预测模型模拟优化锻造工艺方案，避免产生粗晶。
方法 以遗传算法替代梯度下降法优化神经网络各节点的权值和阈值，建立高精度的GA-BP 神经网络晶粒尺寸预测模型，再以轮端轮毂为对象，设计锻造工艺方案并利用Deform 进行微观组织仿真，研究压下速率、坯料初始温度对晶粒尺寸的影响，获得最优方案。
变分图自编码器算法应用于基因-表型关联预测研究
变分图自编码器算法应用于基因-表型关联预测研究摘要：随着基因测序技术的快速发展和普及，基因数据日益增多，如何准确地预测基因与表型之间的关联关系成为了生物信息学领域的研究热点问题。
传统的基于统计学方法或机器学习方法的基因-表型关联预测模型存在许多局限性，如特征选择不够准确、模型易受到抽样偏差的影响等。
为解决这些问题，本文提出了一个基于变分图自编码器的基因-表型关联预测模型。
首先，我们使用变分自编码器对基因数据进行特征提取，得到每个基因的低维度向量表示。
然后，将这些基因向量和表型特征向量构建成图形式的数据表示，使用变分图自编码器进行建模和训练。
最后，我们使用经过训练的模型进行基因-表型关联预测，并对预测结果进行评估和分析。
实验结果表明，我们提出的基于变分图自编码器的基因-表型关联预测模型在预测精度、稳定性和鲁棒性等方面都表现出了明显优势。
因此，该模型具有广泛的实际应用价值，能够为生物医学研究提供有力的支持。
关键词：基因-表型关联预测；自编码器；变分图自编码器；特征提取；模型建模Abstract:With the rapid development and popularization of gene sequencing technology, the amount of gene data is increasing day by day. How to accurately predict the association between genes and phenotypes has become a hot research topic in the field of bioinformatics. Traditional gene-phenotype association prediction models based on statistical or machine learning methods have many limitations, such as inaccurate feature selection and susceptibility to sampling bias. To solve these problems, this paper proposes a gene-phenotype association prediction model based on variational graph autoencoder.Firstly, we use the variational autoencoder to extract features from gene data, obtaining a low-dimensional vector representation of each gene. Then, these gene vectors and phenotype feature vectors are constructed into a graph form of data representation, and the variational graph autoencoder is used for modeling and training. Finally, we use the trained model for gene-phenotype association prediction, and evaluate and analyze the prediction results.Experimental results show that the gene-phenotype association prediction model based on variationalgraph autoencoder proposed in this paper has obvious advantages in prediction accuracy, stability, and robustness. Therefore, the model has a wide range of practical application value and can provide strong support for biomedical research.Keywords: gene-phenotype association prediction; autoencoder; variational graph autoencoder; feature extraction; model buildin。
基于相空间重构和Chebyshev正交基神经网络的短期负荷预测
基于相空间重构和Chebyshev正交基神经网络的短期负荷预测杨胡萍;王承飞;朱开成;胡奕涛【摘要】电力系统短期负荷数据具有明显的混沌特性.在讲述混沌中相空间重构的相关理论后,计算了算例中需要用到的延迟时间和嵌入维数.根据正交多项式优越的泛化和预测性能,在简单介绍Chebyshev正交基函数后,构建了单输入Chebyshev 正交基神经网络预测模型.由于重构后的相空间中每个相点的分量个数不止一个,故所构建的单输入预测模型无法满足要求.为此,在单输入的基础上,设计了基于相空间重构的多输入Chebyshev正交基神经网络动态预测模型,将该模型运用到短期负荷预测中,取得了很高的精度和很好的预测效果.%The electric power system short-term load data has obvious chaos characteristics. After talking about the related theory of phase space reconstruction in chaos, this paper calculates the delay time and embedded dimension needed in later example. According to orthogonal polynomial prediction's superior generalization and forecast performance, the paper constructs a single input neural network forecast model which is based on Chebyshev orthogonal basis after introducing Chebyshev orthogonal basis briefly. Because the point of every phase point in phase space reconstructed is more than one, the foregoing model can not meet the requirements. Therefore, the paper designs a multi input dynamic prediction model of Chebyshev orthogonal basis neural network based on phase space reconstruction. Through applying it to short-term load forecasting, the model gets a high precision and good prediction effect.【期刊名称】《电力系统保护与控制》【年(卷),期】2012(040)024【总页数】5页(P95-99)【关键词】混沌理论;相空间重构;Chebyshev;神经网络;短期负荷预测【作者】杨胡萍;王承飞;朱开成;胡奕涛【作者单位】南昌大学信息工程学院,江西南昌330031;南昌大学信息工程学院,江西南昌330031;江西赣西供电公司,江西新余336500;南昌大学信息工程学院,江西南昌330031【正文语种】中文【中图分类】TM7150 引言短期负荷预测是指一年之内以月、周、天、小时为单位的负荷预测。
基于模糊综合评判的智能行程时间预测算法
2.2
隶属度函数的确定 隶属度函数的确定问题是用模糊综合评判解决具体问题的关键因素之一。隶属度函数构造的合适与
方法 2 基于模糊综合评判的智能行程时间预测算法 （TBFCJ） 。 在方法 2 中， 占有率和交通流量的隶属度函数参数的选择见表 2， 因素权取 A= （0.5， 0.5） 。在清晰化的 过程中将行程时间的 5 个等级： 极短、 较短、 一般、 较长、 极长分别与区间 ［0， 40］ ， ［40， 70］ ， ［70， 100］ ， ［100， 140］ ， ［140， 185］ 对应， 清晰化方法采用加权平均法。
从图 1 和图 2 两种行程时间实际值与预测值的 对比可以看出， 两种预测模型的行程时间预测值都 能反映出行程时间实测值的变化趋势。但从图 3、 图 4 和表 3 可以看出， 模糊综合评判预测算法的预 测效果明显好于模糊回归预测算法， 在最大绝对相
第2期
李庆奎， 等： 基于模糊综合评判的智能行程时间预测算法
图 3 TBFR 预测结果绝对相对误差 Fig.3 Absolute and relative errors of TBFR
图 4 TBFCJ 预测结果绝对相对误差 Fig.4 Absolute and relative errors of TBFCJ Tab.3 项目 模糊回归预测算法 模糊评判预测算法 表 3 误差比较表 Comparison of errors 最大绝对 相对误差 0.081 0.068 平均绝对 相对误差 0.078 0.028 均等系数 0.960 0.983
ai在农业中的应用英语作文
ai在农业中的应用英语作文## AI in Agriculture: A Comprehensive Overview ##。
Agriculture is one of the world's most important industries, providing food and raw materials for a growing global population. However, the industry is facing a number of challenges, including climate change, pests, and diseases. Artificial intelligence (AI) is emerging as a promising tool to help farmers address these challenges and improve agricultural productivity.AI applications in agriculture.There are many potential applications of AI in agriculture, including:1. Crop monitoring.AI can be used to monitor crop health and identify areas of concern. This can be done using a variety of datasources, including satellite imagery, aerial photography, and ground sensors. AI algorithms can then be used to analyze this data and identify patterns that may indicate problems such as disease, nutrient deficiencies, or water stress.2. Pest and disease control.AI can also be used to improve pest and disease control. AI algorithms can be used to identify pests and diseases early on, and to develop targeted management strategies. This can help to reduce the use of pesticides and herbicides, which can have harmful environmental impacts.3. Yield prediction.AI can be used to predict crop yields. This can be done using a variety of data sources, including historical yield data, weather data, and soil data. AI algorithms can thenbe used to develop models that can predict yields with a high degree of accuracy. This information can be used by farmers to make informed decisions about planting,fertilization, and irrigation.4. Precision agriculture.AI is also being used to develop precision agriculture technologies. These technologies allow farmers to manage their fields more precisely, by applying inputs such as fertilizer and water only where and when they are needed. This can help to improve yields and reduce environmental impacts.Benefits of AI in agriculture.The use of AI in agriculture has a number of potential benefits, including:1. Increased productivity.AI can help farmers to increase crop yields and reduce losses from pests and diseases. This can lead to increased food production and lower food prices.2. Reduced environmental impact.AI can help farmers to reduce their use of pesticides and herbicides, which can have harmful environmental impacts. AI can also be used to develop more sustainable farming practices, such as precision agriculture.3. Improved decision-making.AI can provide farmers with valuable information that can help them to make better decisions about their operations. This information can include data on crop health, pest and disease risks, and yield predictions.Challenges to the adoption of AI in agriculture.There are a number of challenges to the adoption of AI in agriculture, including:1. Cost.AI technologies can be expensive to develop andimplement. This can be a barrier for small farmers and farmers in developing countries.2. Data availability.AI algorithms require large amounts of data to train. This data may not always be available, especially for small farmers in developing countries.3. Lack of expertise.AI technologies can be complex to use. Farmers may need training and support to use AI technologies effectively.Conclusion.AI has the potential to revolutionize agriculture. By providing farmers with valuable information and tools, AI can help to increase productivity, reduce environmental impact, and improve decision-making. However, there are a number of challenges to the adoption of AI in agriculture, including cost, data availability, and lack of expertise.These challenges must be addressed in order to ensure that AI can reach its full potential in agriculture.## AI在农业中的应用，全面概述 ##。
方法的英文高级表达
方法的英文高级表达Advanced Expressions for Describing Methods1. Innovative Approach/Methodology:This cutting-edge method employs a unique and groundbreaking approach to tackle the problem at hand.2. Adaptive Strategy:This method is highly flexible and can be adjusted to fit different situations and circumstances.3. Unconventional Technique:This method adopts a non-traditional approach, deviatingfrom conventional methods to achieve superior results.4. Iterative Process:This method emphasizes a continuous and iterative approach, involving repeated cycles of testing and improvements.5. Holistic Approach:6. Rigorous Framework:This method follows a well-structured and rigorous framework, ensuring methodical and meticulous analysis.7. Cross-disciplinary Method:8. Data-driven Methodology:This method relies on extensive data analysis and interpretation to guide decision-making and problem-solving.9. Collaborative Approach:10. Agile Method:This method prioritizes adaptability and responsiveness to changing circumstances, allowing for quick adjustments and improvements.11. Systematic Procedure:This method follows a systematic and step-by-step procedure, ensuring a logical and coherent approach to problem-solving.12. Longitudinal Study:This method involves the collection and analysis of data over an extended period to observe patterns and trends.14. Randomized Control Trial (RCT):This method involves randomly assigning participants to different groups to test the effectiveness of an intervention or treatment.15. Qualitative Research:16. Quantitative Analysis:This method relies on numerical data and statistical techniques to measure and analyze relationships, trends, and patterns.17. Meta-analysis:18. Grounded Theory:This method seeks to generate new theories and concepts from qualitative data, allowing theories to emerge from the data itself.19. Action Research:This method involves implementing and evaluating interventions or changes within a real-world context, aiming to improve practices or solve practical problems.20. Monte Carlo Simulation:21. Genetic Algorithm:This method is an optimization technique inspired by the process of natural selection, using genetic operators to find the best solution among a set of possibilities.22. Neural Network:This method is an artificial intelligence model that attempts to mimic the structure and functioning of the brain, enabling pattern detection and prediction.23. Design Thinking:This method emphasizes empathy, creativity, and iterative problem-solving to create user-centered and innovative solutions.24. Six Sigma:This method is a data-driven approach focused on reducing defects and variability, aiming for near-perfect quality in products or processes.25. Lean Startup:This method advocates for rapid experimentation anditeration in the early stages of a business or project to minimize wasted resources and optimize success.These advanced expressions can help add precision and sophistication when describing methods in various contexts, such as research, problem-solving, and innovation.。
边水凝析气藏高产井见水时间预测新模型
边水凝析气藏高产井见水时间预测新模型明瑞卿;贺会群;胡强法;曹光强;蒲晓莉【摘要】凝析气藏见水时间预测模型考虑反凝析作用,但忽略了高产井近井区域非达西效应的影响,造成见水时间预测产生偏差.针对该问题,运用气水两相渗流力学理论,综合考虑气相非达西效应和反凝析作用的影响,得到边水凝析气藏高产井见水时间预测新模型.研究结果表明:预测边水凝析气藏高产井见水时间需考虑气相非达西效应和反凝析作用的影响,两者均会导致气体流速增大,见水时间变早.将新模型应用于 A气田 S45、AT11-6H、AT11-4井和AT11-2井,其计算结果相对误差小于8%,相比常用计算模型,精度提高了18.10%~39.34%,计算结果与现场实际数据吻合度高.研究成果对深入分析反凝析作用和气相非达西效应对凝析气藏边水推进的影响,预测边水凝析气藏的见水时间有重要的指导意义.%Due to the non-Darcy effect near the wellbore in high-rate well,the water breakthrough time prediction model for condensate gas reservoir by considering retrograde condensation usually leads to a prediction deviation. Based on gas-water two-phase seepage flow theory,both the gas non-Darcy and retrograde condensation effects are comprehensively considered to establish a new model to predict the water breakthrough time of high-rate well in condensate gas reservoir with edge-aquifer. Research indicates that it is necessary to consider the non-Darcy and retrograde condensation effects,both of which will lead to the increase of gas flow rate and the water breakthrough will be advanced. This new prediction model is applied to Well S45,Well AT11-6H,Well AT11-4 and Well AT11-2 in A Gasfield and the corresponding prediction error is less than 8%. Comparing withconventional predic-tion,the prediction precision of this new model is increased by 18.10%~39.34% and the corresponding prediction is in better agreement with actual field data. This research could provide significant guidance for the further analysis of retrograde condensation and non-Darcy effects on edge-aquifer invasion and accurate prediction of water break-through time in condensate gas reservoir with edge-aquifer.【期刊名称】《特种油气藏》【年(卷),期】2018(025)002【总页数】4页(P76-79)【关键词】边水凝析气藏;高产井;非达西效应;反凝析;见水时间;物理模型【作者】明瑞卿;贺会群;胡强法;曹光强;蒲晓莉【作者单位】中国石油勘探开发研究院,北京 100083;中国石油集团工程技术研究院有限公司,北京 102206;中国石油集团工程技术研究院有限公司,北京 102206;中国石油集团工程技术研究院有限公司,北京 102206;中国石油勘探开发研究院,北京100083;中国石油集团工程技术研究院有限公司,北京 102206【正文语种】中文【中图分类】TE3490 引言见水时间预测是边水气藏与底水气藏开发过程中的一个重要问题，国内外研究人员经过大量工作，推导出不同的见水时间计算模型[1-5]，而有关边水凝析气藏见水时间的研究尚处于初期起步阶段。
基于隐马尔可夫模型的脑电信号检测不同脑部疾病(IJIGSP-V11-N10-3)
Detection of Different Brain Diseases from EEG Signals Using Hidden Markov Model
Md. Hasin R. Rabbani, Sheikh Md. Rabiul Islam Department of Electronics & Communication Engineering, Khulna university of
detect the brain diseases from EEG signals by an HMM
probabilistic model. This HMM model is built with a
given initial probability matrix of five different states,
in the various fields of bioinformatics, data mining, pattern recognition, data analysis, wireless networks etc. Some notable works in recent times are protein secondary structure prediction based on a HMM model for data mining [1], offline recognition cursive of Arabic handwritten text without explicit segmentation [2], muscle-computer interface based on HMM state transitions which uses ultrasound sensing [3], action recognition by Gaussian-Mixture HMM (GMM-HMM) model which yields a greater recognition accuracy [4].
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416IEEETRANSACTIONSONSYSTEMS,MAN,ANDCYBERNETIC—PARTC:APPLICATIONSANDREVIEWS,VOL.32,NO.4,NOVEMBER2002AHighPrecisionGlobalPredictionApproachBasedonLocalPredictionApproaches
Shun-FengSu,Member,IEEE,Chan-BenLin,andYen-TsengHsu
Abstract—Traditionalmodel-freepredictionapproaches,suchasneuralnetworksorfuzzymodelsusealltrainingdatawithoutpreferenceinbuildingtheirpredictionmodels.Alternately,onemaymakepredictionsbasedonlyonasetofthemostrecentdatawithoutusingotherdata.Usually,suchlocalpredictionschemesmayhavebetterperformanceinpredictingtimeseriesthanglobalpredictionschemesdo.However,localpredictionschemesonlyusethemostrecentinformationandignoreinformationbearingonfarawaydata.Asaresult,theaccuracyoflocalpredictionschemesmaybelimited.Inthispaper,anovelpredictionapproachtermedastheMarkov–FouriergrayModel(MFGM)isproposed.TheapproachbuildsagraymodelfromasetofthemostrecentdataandaFourierseriesisusedtofittheresidualsproducedbythisgraymodel.Then,theMarkovmatricesareemployedtoencodepossibleglobalinformationgeneratedalsobytheresiduals.ItisevidentthatMFGMcanprovidethebestperformanceamongexistingpredictionschemes.Besides,wealsoimplementedashort-termMFGMapproach,inwhichtheMarkovmatricesonlyrecordedinformationforaperiodoftimeinsteadofalldata.ThepredictionsusingMFGMagainaremoreaccuratethanthoseusingshort-termMFGM.Thus,itisconcludedthattheglobalinformationencodedintheMarkovmatricesindeedcanprovideusefulinformationforpredictions.
IndexTerms—Graymodels,modelfreeestimators,residualcor-rections,timeseriesprediction.
I.INTRODUCTION
TRADITIONALLY,predictionsaremadebasedonsome
predictivemodelsobtainedbystatisticalapproaches[1]–[4].Thoseapproacheshavedemonstratedgoodperfor-manceinvariousapplications.However,thesuccessofthoseapproachesreliesongoodguessesoncertainknowledgeforsystemstobemodeled,suchastheorderorlinearityofthesystems.Recently,asviablealternativestotraditionalstatisticalregressionmodels,neuralnetworks[5]–[9],fuzzysystems[10],[11],andneural-fuzzyparadigms[12]–[16]havebeenemployedinpredictionwithpromisingresults.Infact,bothneuralnetworksandfuzzysystemshavebeenproventobeuniversalapproximators[17],[18].Thoseapproacheshave
ManuscriptreceivedOctober29,2001;revisedMay20,2002.ThispaperwasrecommendedbyAssociateEditorS.E.Shimony.Thisworkwassup-portedinpartbytheNationalScienceCouncilofTaiwanunderGrantNSC89-2218-E-009-091.S.-F.SuiswiththeDepartmentofElectricalEngineering,NationalTaiwanUniversityofScienceandTechnology,Taiwan,R.O.C.(e-mail:su@orion.ee.ntust.edu.tw).C.-B.LiniswiththeDepartmentofInformationManagement,ChangGungInstituteofTechnolgoy,Taiwan,R.O.C.(e-mail:cblin@cc.cgin.edu.tw).Y.-T.HsuiswiththeDepartmentofComputerScienceandInformationEngineering,NationalTaiwanUniversityofScienceandTechnology,Taiwan,R.O.C.(e-mail:ythsu@mail.ntust.edu.tw).DigitalObjectIdentifier10.1109/TSMCC.2002.806745
akeyadvantageovertraditionalstatisticalestimatorsduetotheirmodel-freecharacteristics[19].Thebasicphilosophyofmodel-freeestimatorsisthattheybuildsystemsfrominput–outputpatternsdirectlywithoutusinganypriorknowl-edgeaboutthosesystems.Here,weshalldistinguishpredictioninputsfrompredictionmodels.Predictioninputsaresetsofvariablesfedintopredictionmodelstomakepredictions.Predictionmodelsdefinehowpredictionsaremade.Intheabovemodel-freeapproaches,theconstructionofpre-dictionmodelsisbasedonallavailabletrainingdata.Theytreatalldataequallyinthetrainingprocess,however,ifthepredictionisforatimeseries,themostrecentdatamaycarrymoreinforma-tionthanthosedatafarawayfromthepresent.Thus,whenapre-dictionmodelisconstructedbasedonalltrainingdatawithoutpreference,theresultantpredictionmaynotbeveryaccuratebecausethepredictionaccuracyiscorruptedbythosefarawaydata,whicharesupposedtohavelessrelationshiptothecurrentprediction.Thepredictionapproachesconsideringalltrainingdata,includingthosefarawayfromthepresent,arereferredtoasglobalpredictionschemesinourstudy.Ontheotherhand,onemayconsideranotherkindofapproachinwhichthepredic-tionisonlybasedonthemostrecentdata.Inthoseapproaches,predictionmodelsareconstructedfromcurrentinputswithoutconsideringotherdata.Suchapproachesinfactarecurve-fittingschemesandarereferredtoaslocalpredictionschemesinthispaper.Itisnotedthatlocalpredictionschemescanalsobeem-ployedasglobalpredictionschemesbyconsideringalltrainingdataaspredictioninput.However,itisrarelythecase,duetobadperformance.Onesuccessfulexampleforlocalpredictionapproachesistopredictthefuturebytheso-calledgraymodel[20]and/oritsvariants[29]–[33].Inrecentyears,graymodelshavebeenusedinvariousapplications[23],[24]andhaveshownexcellentperformanceespeciallyfortimeseriesprediction[21].Ascanbeseeninsimulationsreportedlaterinthispaper,goodlocalpredictionschemesusuallyhavebetterperformancethanglobalonesdo.Thoselocalpredictionschemes,however,onlyusethemostrecentinformationandignorecertaininformationbearingonthepast,thus,theaccuracyofthoselocalpredictionschemesmaybelimited.Inthispaper,anovelpredictionapproachisproposedtoincorporateglobalinformationintolocalpredictionschemes.Theremainderofthispaperisorganizedasfollows.InSec-tionII,graymodelingapproachesactingaslocalpredictionschemesarediscussed.AnapproachthatadoptsFourierseriestoincreasethepredictionaccuracyofgraymodelsisalsopre-sented.BasedontheconceptintroducedinSectionII,anovel