微观生态系统仿真试验平台研究
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摘要
生态系统是在一定空间范围内，各生物群落与其环境通过能量流动、物质循环、信息传递而相互作用、相互依存所形成的一个多层次、多因子、多变量的非线性生态学单位。对于生态系统这样的复杂系统，以还原分析为基础的传统建模方法遇到了极大的挑战，仅仅了解系统内部的局部环节而忽略事物间的固有联系，远远不足以解释系统的行为，系统各部分之间的相互作用在解释系统行为方面同等重要。将微观和宏观联系起来的复杂系统理论和基于主体的建模思想为生态系统建模带来了新的启迪。而目前基于复杂系统理论的微观生态系统建模需要解决以下问题1．如何从复杂的微观生态系统中获取基本要素的量化指标；2．如何抽取生态要素之间的互作关系；3．如何为生态过程控制提供仿真试验平台。
本文在国家863计划“智能化信息处理技术”课题的支持下，以现代系统理论、方法和现代计算机技术为基础，针对农田尺度的作物生产微观生态系统建模仿真的需要，研究包括微观生态系统数据采集、信息处理、建模仿真和控制决策等理论和技术方法，构建微观生态系统仿真实验平台，为研究微观生态系统行为及其演化规律，提供实验技术支撑。具体研究内容概括如下：
1．针对微观生态系统信息采集的特点，利用现代信息技术，设计并实现了一个微观生态系统信息采集平台，平台集成有多种传感器、测量设备和GPRS模块，能够实现微观生态数据，包括土壤、环境以及生物本身的相关信息的实时采集和远程传输。
2．研究了生态系统的复杂性，并结合复杂适应系统理论，分析了生态系统的复杂适应性典型特征。在适应性主体概念和复杂适应系统建模方法研究的基础上，结合具体的生态系统实例，提出了微观生态系统的概念模型、表示模型和计算模型，并探讨了复杂性理论在生态学研究中的应用方法。
3．将生物中协同进化的概念引入生态系统研究中，研究生态系统内的生物个体、物种、种群、群落的内部，以及它们之间，它们与环境的协同现象、协同进化机理和进化方法，探讨建立生态系统仿真模型的理论基础。在此基础上建立微观生态系统主要生态过程的演进模型。
4．研究了适于生态系统建模和仿真的基于Agent的建模思想，以及元胞自动机的工作原理。利用REPAST工具建立了微观生态系统的元胞自动机模型，对生态系统的主要生态过程进行了仿真试验，仿真效果良好。
6．重点研究了微观生态系统的可配置仿真平台的系统体系，开发了可以模拟微观生态系统多种行为的仿真模块。通过集成微观生态系统的数据采集、规则抽取、建模仿真、决策控制等功能模块，形成了一种可配置的微观生态系统仿真试验平台。
本文研究的部分内容已通过成果鉴定，并获科技部农业成果转化基金的进一步支持，有望通过完善得以实际应用。
The ecosystem is a multi-level, multifactor , multivariable and non-linear ecologic unit which covers a certain spatial scope, in which various living things communities and the environment interact with each other through the energy flow, the circulation of materials and the information transmission, and depend on each other mutually. Regarding such complex system, understanding merely the interior partial links in the system is insufficient to explain the system behavior, knowing the interaction of various parts in the system is equally important for the explanation of system behavior, so the theory of complex system and thought of agent based modeling, which bring the microcosmos and macrocosmos together, enlighten the ecosystem modeling. But at present, the following problems should be solved in the ecosystem modeling based on the complex system theory. 1. How to acquire the quantitative indexes of the ecologic factors in the microscopic ecosystem. 2. How to extract the correlations among the ecologic factors. 3. How to provide the ecosystem researchers or administrators with a practical platform for ecosystem modeling and simulation.
Based on the modern system theory and its method as well as the modern computer technology, and for meeting the crops production microscopic ecosystem modeling and simulation need, studies including the microscopic ecosystem data acquisition, information processing, modeling and simulation and control decision-making are carried out, and the experiment platform for the microscopic ecosystem modeling and simulation is developed, which can support the study of the microscopic ecosystem. The research results are as followed:
1. Microscopic ecosystem information deals with soil, environment as well as living things themselves. When modeling and simulating , many kinds of information should be obtained. According to the characteristics of the microscopic ecosystem information acquisition, and based on the modern information technology, a microscopic ecosystem information acquisition platform is built, which integrates some sensors and the GPRS module, to realize microscopic ecologic data real-time gathering and the long-distance transmission.
2. Based on the complexity theory, the concept of adaptive agents and the basic characteristics of complex adaptive system, and from the viewpoint of ecosystem complexity, the ecosystem as a typical complex system is analyzed, and the method of complex adaptive system theory and its application to the ecosystem are discussed.
3. In the nature, any species or individuals, any population and communities all exist in a certain ecosystem. In ecosystem, all individuals, species, population and communities compete and cooperate with each other. Study of such coordination and competition relations, is the basis of the ecosystem simulation model, so the paper discusses the co-evolution mechanism and the evolution method of all parts in ecosystem, and explores the ecosystem evolution rules.
4. Agent based modeling and simulation method is a new way for complex system modeling and simulation. This paper introduces the Agent-based modeling method suitable for the ecosystem modeling and the simulation, as well as complex adaptive system model frame. It also introduces the structure of Cell-Automation and its mechanism, as well as the CA model of microscopic ecosystem. Meanwhile the paper introduces the platform for the modeling and simulation of multi-agent system.
5. At present, a lot of programming work is needed when using complex system modeling tools for ecosystem modeling, so it is difficult for the ordinary user to use the tools .At the same time, there is no a comprehensive tool for the ecologic data acquisition, rule extraction, as well as model validation. The paper focuses on the architecture of the configurable platform of ecosystem simulation, and develops a series of ecosystem models for ecosystem study.
Some achievements in this project has been appraised and also funded by agricultural science and technology achievements transformation fund program.
本文在国家863计划“智能化信息处理技术”课题的支持下，以现代系统理论、方法和现代计算机技术为基础，针对农田尺度的作物生产微观生态系统建模仿真的需要，研究包括微观生态系统数据采集、信息处理、建模仿真和控制决策等理论和技术方法，构建微观生态系统仿真实验平台，为研究微观生态系统行为及其演化规律，提供实验技术支撑。具体研究内容概括如下：
1．针对微观生态系统信息采集的特点，利用现代信息技术，设计并实现了一个微观生态系统信息采集平台，平台集成有多种传感器、测量设备和GPRS模块，能够实现微观生态数据，包括土壤、环境以及生物本身的相关信息的实时采集和远程传输。
2．研究了生态系统的复杂性，并结合复杂适应系统理论，分析了生态系统的复杂适应性典型特征。在适应性主体概念和复杂适应系统建模方法研究的基础上，结合具体的生态系统实例，提出了微观生态系统的概念模型、表示模型和计算模型，并探讨了复杂性理论在生态学研究中的应用方法。
3．将生物中协同进化的概念引入生态系统研究中，研究生态系统内的生物个体、物种、种群、群落的内部，以及它们之间，它们与环境的协同现象、协同进化机理和进化方法，探讨建立生态系统仿真模型的理论基础。在此基础上建立微观生态系统主要生态过程的演进模型。
4．研究了适于生态系统建模和仿真的基于Agent的建模思想，以及元胞自动机的工作原理。利用REPAST工具建立了微观生态系统的元胞自动机模型，对生态系统的主要生态过程进行了仿真试验，仿真效果良好。
6．重点研究了微观生态系统的可配置仿真平台的系统体系，开发了可以模拟微观生态系统多种行为的仿真模块。通过集成微观生态系统的数据采集、规则抽取、建模仿真、决策控制等功能模块，形成了一种可配置的微观生态系统仿真试验平台。
本文研究的部分内容已通过成果鉴定，并获科技部农业成果转化基金的进一步支持，有望通过完善得以实际应用。
The ecosystem is a multi-level, multifactor , multivariable and non-linear ecologic unit which covers a certain spatial scope, in which various living things communities and the environment interact with each other through the energy flow, the circulation of materials and the information transmission, and depend on each other mutually. Regarding such complex system, understanding merely the interior partial links in the system is insufficient to explain the system behavior, knowing the interaction of various parts in the system is equally important for the explanation of system behavior, so the theory of complex system and thought of agent based modeling, which bring the microcosmos and macrocosmos together, enlighten the ecosystem modeling. But at present, the following problems should be solved in the ecosystem modeling based on the complex system theory. 1. How to acquire the quantitative indexes of the ecologic factors in the microscopic ecosystem. 2. How to extract the correlations among the ecologic factors. 3. How to provide the ecosystem researchers or administrators with a practical platform for ecosystem modeling and simulation.
Based on the modern system theory and its method as well as the modern computer technology, and for meeting the crops production microscopic ecosystem modeling and simulation need, studies including the microscopic ecosystem data acquisition, information processing, modeling and simulation and control decision-making are carried out, and the experiment platform for the microscopic ecosystem modeling and simulation is developed, which can support the study of the microscopic ecosystem. The research results are as followed:
1. Microscopic ecosystem information deals with soil, environment as well as living things themselves. When modeling and simulating , many kinds of information should be obtained. According to the characteristics of the microscopic ecosystem information acquisition, and based on the modern information technology, a microscopic ecosystem information acquisition platform is built, which integrates some sensors and the GPRS module, to realize microscopic ecologic data real-time gathering and the long-distance transmission.
2. Based on the complexity theory, the concept of adaptive agents and the basic characteristics of complex adaptive system, and from the viewpoint of ecosystem complexity, the ecosystem as a typical complex system is analyzed, and the method of complex adaptive system theory and its application to the ecosystem are discussed.
3. In the nature, any species or individuals, any population and communities all exist in a certain ecosystem. In ecosystem, all individuals, species, population and communities compete and cooperate with each other. Study of such coordination and competition relations, is the basis of the ecosystem simulation model, so the paper discusses the co-evolution mechanism and the evolution method of all parts in ecosystem, and explores the ecosystem evolution rules.
4. Agent based modeling and simulation method is a new way for complex system modeling and simulation. This paper introduces the Agent-based modeling method suitable for the ecosystem modeling and the simulation, as well as complex adaptive system model frame. It also introduces the structure of Cell-Automation and its mechanism, as well as the CA model of microscopic ecosystem. Meanwhile the paper introduces the platform for the modeling and simulation of multi-agent system.
5. At present, a lot of programming work is needed when using complex system modeling tools for ecosystem modeling, so it is difficult for the ordinary user to use the tools .At the same time, there is no a comprehensive tool for the ecologic data acquisition, rule extraction, as well as model validation. The paper focuses on the architecture of the configurable platform of ecosystem simulation, and develops a series of ecosystem models for ecosystem study.
Some achievements in this project has been appraised and also funded by agricultural science and technology achievements transformation fund program.
引文
[1]戴汝为，主编．复杂性研究[M]．北京：中国科学院复杂系统与智能科学实验室，1999
[2]戴汝为．复杂巨系统科学——门21世纪的科学[J]．自然杂志，1997，19(1)
[3]许囡志主编，系统科学与工程研究[M]．上海：上海科技教育出版社．2000．10
[4]成思危主编，复杂性科学探索(论文集)[C]．北京：民主与建设出版社，1998．8
[5]约翰·霍兰著．涌现——从混沌到有序[M]，陈禹等译．上海：上海科学技术出版社，2001．11
[6]米歇尔·沃尔德罗普．复杂——诞生于秩序与混沌边缘的科学[M]．陈玲译，上海：三联书店， 1997
[7]钱学森，于景元，戴汝为．一个科学新领域——开放的复杂巨系统及其方法论[J]．自然杂志，??1990,13(1), 3-10
[8] Olson, R.L., Sequeira, R.A., 1995. Emergent computation and the modeling and management of ecological systems. Computer. Electron. Agric. 12,183-209.
[9] P.Coveney, R. Highfield. Frontiers of Complexity: The Search for Order in a Chaotic World. New York, NY: Fawcett Columbine, 1995
[10] 谭跃进, 高世楫, 周曼殊.系统学原理[M]. 长沙:国防科技大学出版社, 1995
[11] 成思危. 复杂科学与系统工程[J]. 管理科学学报, 1999 ,2 (2) :1-71
[12] N.Gessler. Growing Artificial Societies - Social Science from the Bottom Up. 1997, Artificial Life 3:237-42
[13] M. M. Waldrop, Complexity: the emerging science at the edge of order and chaos. New York:Simon & Schuster, 1992.
[14] Jeffrey Goldstein. Emergence as a Construct: History and Issues[J]. Emergence , 1999,1 (1) .49 -72.
[15] Lanton CG. Artificial Life(Volume VI) [M]. CA: Addison-Wesley ,1989.15-17.
[16] 沈学华,杨献忠,邹志华,王建新.我工生命的研究[J].南京大学学报(自然科学版) ,2000,36(11) : 120—121.
[17] 涂晓媛. 人工鱼——计算机动画的人工生命方法[M]. 北京:清华大学出版社.2001
[18] 刘健勤.人工生命理论及其应用[M].冶金工业出版社. 1997
[19] 涂晓媛,陈泓娟,涂序彦.计算机动画的人工生命方法——"人工鱼"的建模和生成[J]. 软件世界，2000，(4)：51-53。
[20] 蒋培, 王洪燕，杨敬安. 人工生命仿真的分析 [J]. 长沙铁道学院学报, 1999,17(2) : 34-39
[21] Nelson Minar, Roger Burkhart, Chris Langton, Manor Askenazi. A Top-Down Introduction To Implementing an Experiment Using Swarm.http://www.santafe.edu/projects/swarm.
[22] Nelson Minar, Roger Burkhart, Chris Langton, Manor Askenazi. A Toolkit for Building Multi-agent Simulations. SFI 工作论文 96-04-2 1996
[23] Benedikt Stefansson. Swarm:An Object Oriented Simulation Platform Applied to Markets and Organizations Evolutionary Programming VI Lecture Notes in Computer Science Vol 1213 Springer-Verlag NY 1997
[24] Langton CG Artificial life [A]. In: CG Langton (Editor), Artificial Life[C]. Addison Wesley,??Redwood City, CA (1989b), 1 -47
[25] J. H. Holland, J. H. Miller, "Artificial adaptive agents and economic theory," American Economic Review, 1991,81:365-370
[26]陈余年，方美琪．信息系统工程中的面向对象方法．北京：清华大学出版社，1999
[27]张知彬，进化与生态复杂性[M]．北京：海洋出版社，2002．4
[28]任晓明，罗保华从复杂系统理论视角看人工生命的发展[J]自然辩证法研究Vol．19 No．4 Apr．,2003
[1]孙忠富，陈青云，吴毅明．计算机在现代温室中的应用现状及前景[J]．农业工程学报， 1998，14：22-27
[2]韩世进，张乐年．基RS485的多路数据采集系统[J]现代电子技术，2003，11
[3]于新，王洪明，曾为民．远程数据采集系统的设计与实现[J]信息技术．2003，4
[4]张秀彬张颖璐．分布数据采集系统扩展法及评估原则[J]．上海交通大学学报， 1996，30(1)：101-107
[5]张秀彬孙威．检控通道扩展与混合编程研究[J]．电子测量与仪器学报，1997，11(3)：22—27
[6]张丽，阔永红，傅丰林．基于PCI9054的数据采集系统的实现．现代电子技术， 2007，30(6)：144-146
[7]于锋 侯永海 王建国．基于PCI-1714的高速数据采集系统方案设计[J]．微计算机信息，2005，8：60-62
[1]霍兰，隐秩序．周晓牧，韩晖译．上海：上海科技教育出版社，2000
[2]霍兰，涌现．陈禹等译．上海：上海科学出版社，2001
[3]陈禹，复杂适应系统(CAS)理论及其应用——由来、内容与启示[J]．系统辩证学学报， 2001，9(4)
[4]许国志主编，系统科学．上海：上海科技教育出版社，2000．9
[5]欧阳莹之。复杂系统理论基础．上海：上海科技教育出版社，2002．10
[6]成思危主编，复杂性科学探索(论文集)．北京：民主与建设出版社．1998．8
[7]张良桥，冯从文．理性与有限理性：论经典博弈理论与进化博弈理论之关系[J]．世界经济， 2001，(8)：74-78
[8]应尚军等．元胞自动机及其在经济学中的应用[J]．中国管理科学,2000,8(11)：272-278
[9]陈道江．经济学的新发展：演化经济理论的回顾与展望[J]．学海,2004,(1)：155-161
[10]汪浩瀚．从均衡走向演化—经济学范式的演进[J]．财经问题研究,2003,(3)：16-19
[11]约翰·H·霍兰德．隐秩序-适应性造就复杂性[M]．上海：上海科技教育出版社,2000
[12]王正中．基于演化的复杂系统建模与仿真研究[J]．系统仿真学报,2003,15(7)：905-909
[13] R.Axtell, Growing Artificial Societies-Social Science from the Bottom Up. Washington: Brookings Institution Press. Cambridge, MA: MIT Press. 1996
[14] J. H. Holland, J. H. Miller. Artificial adaptive agents and economic theory. American Economic Review, 1991,81:365-370
[15] M. M. Waldrop, Complexity: the emerging science at the edge of order and chaos. New York:Simon & Schuster, 1992
[16] R. Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton, NJ: Princeton University Press, 1997
[1]G．C．瓦利，G．R．格拉德韦尔，M．P．哈塞尔．昆虫种群生态学分析方法．李祖荫等译．科学出 版社，1981
[2]张炳根．生态学数学模型．青岛海洋大学出版社，1990
[3] 李自珍，韩晓卓，李文龙。具有生态位构建作用的种群进化动力学模型及其应用研究[J]。应用数学与力学，2006，27(3):293-197.
[4] J. H. Holland. J. H. Miller. "Artificial adaptive agents and economic theory," American Economic Review, vol. 81, pp. 365-370, 1991.
[5] Heiko Balzter, Paul W. Braun, Wolfgang Ko¨hler. Cellular automata models for vegetation dynamics Ecological Modeling, 1998,107:113-125
[6] R.L Olson, R.A Sequeira. Emergent computation and the modeling and management of ecological systems. Computer. Electron. Agric. 1995,12:183-209
[7] Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM. Journal of Artificial Societies and Social Simulation, 1998,1(2)
[8] C.P.William, W.B.Paul, F.F.Knowlton. An individual-based model of canid populations: modeling territoriality and social structure. Ecological Modeling, 2003,166:109-121
[9] Dangles D L, Louis J Gross. Individual-based models and approaches in ecology [M]. Chapman & Hall, NY, 1992.
[10] Jan Dijkstra, Harry Timmermans. Towards a multi-agent model for visualizing simulated user behavior to support the assessment of design performance. Automation in Construction , 2002(11):135-145
[11] Sa Li, Fanlun Xiong, Jing Ding, Honghao Zuo. A Swarm-Based Model for Weed-Crop System.2004 CIGR International Conference, Beijing, China, 11-14 October 2004
[12] C.P.William, W.B.Paul, F.F.Knowlton. An individual-based model of canid populations: modeling territoriality and social structure. Ecological Modeling, 2003,166:109-121
[13] Thomas Berger. Agent-based spatial models applied to agriculture: a simulation Economics, 2001 (25): 245-260
[14] Ferdinando Villa, Robert Costanza. Design of multi-paradigm integrating modeling tools for ecological research. Environmental Modeling & Software, 2000 (15): 169-177
[15] Jianguo Wu, John L. David, spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modeling, 2002 (153): 7-26
[16] L.Parrott. The EcoCyborg Project: A model of an artificial ecosystem. M.Sc. thesis, Dept.of Agricultural and Bio-systems engineering, McGill University, Montreal, 2000
[17] Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM.Journal of Artificial Societies and Social Simulation, 1998, 1(2)
[18] L. Berec. On individual-based models for single species population growth, predator-prey interaction and optimal foraging, 1999,1998.
[1]李群，宣慧玉．基于Agent仿真技术在经济建模中的应用[J]．系统工程理论方法应用，2001， 10(3)：221-225．
[2]毛新军等．智能体的理论研究[J]．计算机科学，1997,24(5)：63-67．
[3]曹先彬，罗文坚，王煦法．基于生态种群竞争模型的协同进化[J]．软件学报，2001，12(4)：556562
[4]谭跃进，邓宏钟．复杂适应系统理论及其应用研究[J]．系统工程，2001，19(5)：1-6
[5]张守一，SWARM及其在经济研究中的应用[J]．数量经济技术经济研究。2001，18(1)：9497
[6]王小平，曹立明．遗传算法——理论、应用与软件实现[M]，西安：西安交通大学出版社，2002．1
[7]李宏亮，程华等．复杂自适应系统的描述及其分布仿真框架[A]，计算机研究与发展，2002， 39(10)：1349-1354
[8]胡代平，王洗尘．建立支持宏观经济决策研讨厅的预测模型系统[J]．系统工程学报，2001，16(5)：335-339
[9]冯珊，唐超，阂君，沈冲．用于复杂系统建模与仿真的面向智能体技术[J]．管理科学学报，1999， 2(2)：717
[10] 邓宏钟，谭跃进，迟妍。一种复杂系统研究方法——基于多智能体的整体建模仿真方法[J]。系统工程．2000，18(4)：73-7
[11]邓宏钟，谭跃进．基于复杂适应系统的自动建模方法研究，计算机仿真，2002，19(6)
[12]杨敏，邱菀华．复杂决策系统研究——框架及其方法．系统工程理论与实践,2002,9(9)
[13]王迪兴，复杂适应系统的共性描述．系统辩证学学报．2001，9(4)
[14]史忠植．智能主体及其应用[M]．北京：科学出版社
[15]许国志主编．系统科学．上海：上海科技教育出版社，2000，9
[16]范玉顺，曹军威编著．复杂系统的面向对象建模、分析与设计，北京：清华大学出版社，2000
[17]唐锡晋．模型集成．系统工程学报，2001，16(5)：322-328
[18]张永安，白学志．复杂系统的重要研究工具——细胞自动机及其应用[J]．自然杂志，1998,20(4)：192-196
[19]应尚军，魏一鸣，范英等．基于元胞自动机的股票市场投资行为模拟，系统工程学报，2001，16(5)
[20]丁浩，杨小平．SWARM—一个支持人工生命建模的面向对象模拟平台．系统仿真学报，2002，14(5):569-572
[26] Bayard R J, Info S Leuth. Agent-based semantic integration of information in open and dynamic environments[A]. Proceeding of the ACM SIGMOD Conference on Management of Data, 1997,195-205
[27] Steven F. Railsback. Concepts from complex adaptive systems as a framework for individual-based modeling. Ecological Modelling, 2001, 139:47-62
[28] Adam L.omnicki. Individual-based models and the individual-based approach to population ecology. Ecological Modelling, 1999,115:191-198
[29] S. Y. Auyang. Foundations of Complex-System Theories in Economics, Evolutionary Biology, and Statistical Physics. Cambridge, England: Cambridge University Press, 1998
[30] Gregg Hartvigsen, Ann Kinzig, Garry Peterson. Use and Analysis of Complex Adaptive Systems in Ecosystem Science. Ecosystems, 1998) 1:427-430
[31] Roger Burkhart. The swarm : multi-agent simulation system[C]. Position Paper for OOPSLA'94 Workshop on "The Object Engine", 1994
[32] MINAR, M., Burkhart, R., Langton, C, Askenazy, M. 1996, The Swarm Simulation System: AToolkit for Building Multi-agent Simulations. Santa Fe Institute.
[33]Benedikt Stefansson. Swarm- An Object Oriented Simulation Platform Applied to Markets and Organizations[R]. Evolutionary Programming VI Lecture Notes in Computer Science Vol. 1213.Springer-Verlag, NY, 1997
[34]Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM. Journal of Artificial Societies and Social Simulation, 1998,1(1)
[1] 王土同．模糊专家系统、模糊神经网络及其程序设计．上海：上海科技出版社，1998
[2] 刘康平，朱海萍．告警关联与故障诊断专家系统研究与实现．计算机工程，2002，28(6)：11-18
[3] 蔡自兴，徐光祜．人工智能及其应用．北京：清华大学出版社，2003
[4] 孔繁胜．知识库系统原理．杭州：浙江大学出版社，2000
[5] 杨炳儒，申京涛．基于知识发现的广义诊断型专家系统GDESKD新构造．模式识别与人工智能，1999．12(4)：373～380．
[6] Nils J N.Artificail Intelligence:A New Synthesis[M].Morgan Kaufmann Publishers Inc.1998
[7] Hori K. An Ontology of Strategic Knowledge : Key Concepts and Applications. Knowledge-based Systems, 2000, (13): 369-374
[8] Ishino Y, Hori K, Nakasuka S. Concept Development of Consumer Goods Utilizing Strategic Knowledge. Knowledge-based Systems, 2000, (13): 417-427.
[9] Karp P D.,Wilkins D C. An analysis of the distinction between deep and shallow expert systems. International Journal of Expert Systems, 1989,2(1): 1～32
[10] Joseph G.Gary R.Expert systems:Principles and Programming.second edition.Boston:PWS Publishing Company, 1993
[11] Daniel Z.,Shawn P. Fish disease diagnosis program-problems and some solutions.Aquacultural Engineering ,2000 , (23) :3 — 111
[12] Harmon P., Maus R. Morrissey W. Expert Systems Tools and Applications[M]. New York: Wiley, 1988.14-20.
[13] Russell S. .Norvig, P. Artificial Intelligence: A Modern Approach, Prentice Hall, New Jersey, 1995
[14] Lyndon M. Tan C, An object-oriented knowledge base for multi-domain expert systems[J].Expert System with Application, 1995, Vol. 8, No. 1,177-185
[15] Kyung H. Object-oriented approach to a knowledge-based structural design system[J].Expert System with Application, 1996, Vol.10,No.2,223-231
[16] Harmon P. Expert system building tools for object-oriented development[J].Journal of Object-Oriented Programming, 1991, Vol. 4, No. 3, 61-63
[17] Carver,S.J.,Integrating multi-criteria evaluation with GIS,International journal of geographical information systems, 1991 (5)
[18]Malczewski,J.,A GIS-based approach to multiple criteria group decision-making.lnternational journal of geographic information systems, 1996(10)
[19]Zhao Gengxing,Li Yuhuan.Li Qing.Quantitative and automatic evaluation method supported by GIS of agricultural land.Transaction of the CASE, 1999,15(1)
[20] Joseph Giarratano, Gary Riley. Expert Systems: Principles and Programming. 2nd edition. USA: PWS Publishing Company. 1993.
[21] Zlatareva, N.P. A Refinement Framework to Support Validation and Maintenance of Knowledge-based Systems. Expert Systems with Application. 1998,15: 245-252.
[22]Fayyad, U. Data Mining and Knowledge Discovery: Making Sense out of Data. IEEE Expert,1996,11(5): 20-25.
[23]J.Han,Y.Cai, N.Cercone. Knowledge Discovery in Databases- An Attribute Oriented Approach. In Proc, of the VLDB Conference, pp.547~559, Vancouver, British Columbia, Canada, 1992.
[24] Yang Bing-ru, Shen Jiang-tao. KDD* Based on Double-Base Cooperating Mecha-nism and Its Realization of Software. Journal of System Engineering and Electronics, 1999,10(4): 1-9.
[25] Yang Bing-ru. KD(D&K) And Double Bases Cooperating Mechanism. Journal of System Engineering and Electronics, 1999,10(2): 48-54.
[26] Yang Bing-ru, Liu Fa-sheng, Shen Jiang-tao. ESDK- A New Structure of Expert System Based on Knowledge Discovery. Journal of University of Science and Technology Beijing, 2000,7(1): 63-67.
[2]戴汝为．复杂巨系统科学——门21世纪的科学[J]．自然杂志，1997，19(1)
[3]许囡志主编，系统科学与工程研究[M]．上海：上海科技教育出版社．2000．10
[4]成思危主编，复杂性科学探索(论文集)[C]．北京：民主与建设出版社，1998．8
[5]约翰·霍兰著．涌现——从混沌到有序[M]，陈禹等译．上海：上海科学技术出版社，2001．11
[6]米歇尔·沃尔德罗普．复杂——诞生于秩序与混沌边缘的科学[M]．陈玲译，上海：三联书店， 1997
[7]钱学森，于景元，戴汝为．一个科学新领域——开放的复杂巨系统及其方法论[J]．自然杂志，??1990,13(1), 3-10
[8] Olson, R.L., Sequeira, R.A., 1995. Emergent computation and the modeling and management of ecological systems. Computer. Electron. Agric. 12,183-209.
[9] P.Coveney, R. Highfield. Frontiers of Complexity: The Search for Order in a Chaotic World. New York, NY: Fawcett Columbine, 1995
[10] 谭跃进, 高世楫, 周曼殊.系统学原理[M]. 长沙:国防科技大学出版社, 1995
[11] 成思危. 复杂科学与系统工程[J]. 管理科学学报, 1999 ,2 (2) :1-71
[12] N.Gessler. Growing Artificial Societies - Social Science from the Bottom Up. 1997, Artificial Life 3:237-42
[13] M. M. Waldrop, Complexity: the emerging science at the edge of order and chaos. New York:Simon & Schuster, 1992.
[14] Jeffrey Goldstein. Emergence as a Construct: History and Issues[J]. Emergence , 1999,1 (1) .49 -72.
[15] Lanton CG. Artificial Life(Volume VI) [M]. CA: Addison-Wesley ,1989.15-17.
[16] 沈学华,杨献忠,邹志华,王建新.我工生命的研究[J].南京大学学报(自然科学版) ,2000,36(11) : 120—121.
[17] 涂晓媛. 人工鱼——计算机动画的人工生命方法[M]. 北京:清华大学出版社.2001
[18] 刘健勤.人工生命理论及其应用[M].冶金工业出版社. 1997
[19] 涂晓媛,陈泓娟,涂序彦.计算机动画的人工生命方法——"人工鱼"的建模和生成[J]. 软件世界，2000，(4)：51-53。
[20] 蒋培, 王洪燕，杨敬安. 人工生命仿真的分析 [J]. 长沙铁道学院学报, 1999,17(2) : 34-39
[21] Nelson Minar, Roger Burkhart, Chris Langton, Manor Askenazi. A Top-Down Introduction To Implementing an Experiment Using Swarm.http://www.santafe.edu/projects/swarm.
[22] Nelson Minar, Roger Burkhart, Chris Langton, Manor Askenazi. A Toolkit for Building Multi-agent Simulations. SFI 工作论文 96-04-2 1996
[23] Benedikt Stefansson. Swarm:An Object Oriented Simulation Platform Applied to Markets and Organizations Evolutionary Programming VI Lecture Notes in Computer Science Vol 1213 Springer-Verlag NY 1997
[24] Langton CG Artificial life [A]. In: CG Langton (Editor), Artificial Life[C]. Addison Wesley,??Redwood City, CA (1989b), 1 -47
[25] J. H. Holland, J. H. Miller, "Artificial adaptive agents and economic theory," American Economic Review, 1991,81:365-370
[26]陈余年，方美琪．信息系统工程中的面向对象方法．北京：清华大学出版社，1999
[27]张知彬，进化与生态复杂性[M]．北京：海洋出版社，2002．4
[28]任晓明，罗保华从复杂系统理论视角看人工生命的发展[J]自然辩证法研究Vol．19 No．4 Apr．,2003
[1]孙忠富，陈青云，吴毅明．计算机在现代温室中的应用现状及前景[J]．农业工程学报， 1998，14：22-27
[2]韩世进，张乐年．基RS485的多路数据采集系统[J]现代电子技术，2003，11
[3]于新，王洪明，曾为民．远程数据采集系统的设计与实现[J]信息技术．2003，4
[4]张秀彬张颖璐．分布数据采集系统扩展法及评估原则[J]．上海交通大学学报， 1996，30(1)：101-107
[5]张秀彬孙威．检控通道扩展与混合编程研究[J]．电子测量与仪器学报，1997，11(3)：22—27
[6]张丽，阔永红，傅丰林．基于PCI9054的数据采集系统的实现．现代电子技术， 2007，30(6)：144-146
[7]于锋 侯永海 王建国．基于PCI-1714的高速数据采集系统方案设计[J]．微计算机信息，2005，8：60-62
[1]霍兰，隐秩序．周晓牧，韩晖译．上海：上海科技教育出版社，2000
[2]霍兰，涌现．陈禹等译．上海：上海科学出版社，2001
[3]陈禹，复杂适应系统(CAS)理论及其应用——由来、内容与启示[J]．系统辩证学学报， 2001，9(4)
[4]许国志主编，系统科学．上海：上海科技教育出版社，2000．9
[5]欧阳莹之。复杂系统理论基础．上海：上海科技教育出版社，2002．10
[6]成思危主编，复杂性科学探索(论文集)．北京：民主与建设出版社．1998．8
[7]张良桥，冯从文．理性与有限理性：论经典博弈理论与进化博弈理论之关系[J]．世界经济， 2001，(8)：74-78
[8]应尚军等．元胞自动机及其在经济学中的应用[J]．中国管理科学,2000,8(11)：272-278
[9]陈道江．经济学的新发展：演化经济理论的回顾与展望[J]．学海,2004,(1)：155-161
[10]汪浩瀚．从均衡走向演化—经济学范式的演进[J]．财经问题研究,2003,(3)：16-19
[11]约翰·H·霍兰德．隐秩序-适应性造就复杂性[M]．上海：上海科技教育出版社,2000
[12]王正中．基于演化的复杂系统建模与仿真研究[J]．系统仿真学报,2003,15(7)：905-909
[13] R.Axtell, Growing Artificial Societies-Social Science from the Bottom Up. Washington: Brookings Institution Press. Cambridge, MA: MIT Press. 1996
[14] J. H. Holland, J. H. Miller. Artificial adaptive agents and economic theory. American Economic Review, 1991,81:365-370
[15] M. M. Waldrop, Complexity: the emerging science at the edge of order and chaos. New York:Simon & Schuster, 1992
[16] R. Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton, NJ: Princeton University Press, 1997
[1]G．C．瓦利，G．R．格拉德韦尔，M．P．哈塞尔．昆虫种群生态学分析方法．李祖荫等译．科学出 版社，1981
[2]张炳根．生态学数学模型．青岛海洋大学出版社，1990
[3] 李自珍，韩晓卓，李文龙。具有生态位构建作用的种群进化动力学模型及其应用研究[J]。应用数学与力学，2006，27(3):293-197.
[4] J. H. Holland. J. H. Miller. "Artificial adaptive agents and economic theory," American Economic Review, vol. 81, pp. 365-370, 1991.
[5] Heiko Balzter, Paul W. Braun, Wolfgang Ko¨hler. Cellular automata models for vegetation dynamics Ecological Modeling, 1998,107:113-125
[6] R.L Olson, R.A Sequeira. Emergent computation and the modeling and management of ecological systems. Computer. Electron. Agric. 1995,12:183-209
[7] Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM. Journal of Artificial Societies and Social Simulation, 1998,1(2)
[8] C.P.William, W.B.Paul, F.F.Knowlton. An individual-based model of canid populations: modeling territoriality and social structure. Ecological Modeling, 2003,166:109-121
[9] Dangles D L, Louis J Gross. Individual-based models and approaches in ecology [M]. Chapman & Hall, NY, 1992.
[10] Jan Dijkstra, Harry Timmermans. Towards a multi-agent model for visualizing simulated user behavior to support the assessment of design performance. Automation in Construction , 2002(11):135-145
[11] Sa Li, Fanlun Xiong, Jing Ding, Honghao Zuo. A Swarm-Based Model for Weed-Crop System.2004 CIGR International Conference, Beijing, China, 11-14 October 2004
[12] C.P.William, W.B.Paul, F.F.Knowlton. An individual-based model of canid populations: modeling territoriality and social structure. Ecological Modeling, 2003,166:109-121
[13] Thomas Berger. Agent-based spatial models applied to agriculture: a simulation Economics, 2001 (25): 245-260
[14] Ferdinando Villa, Robert Costanza. Design of multi-paradigm integrating modeling tools for ecological research. Environmental Modeling & Software, 2000 (15): 169-177
[15] Jianguo Wu, John L. David, spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modeling, 2002 (153): 7-26
[16] L.Parrott. The EcoCyborg Project: A model of an artificial ecosystem. M.Sc. thesis, Dept.of Agricultural and Bio-systems engineering, McGill University, Montreal, 2000
[17] Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM.Journal of Artificial Societies and Social Simulation, 1998, 1(2)
[18] L. Berec. On individual-based models for single species population growth, predator-prey interaction and optimal foraging, 1999,1998.
[1]李群，宣慧玉．基于Agent仿真技术在经济建模中的应用[J]．系统工程理论方法应用，2001， 10(3)：221-225．
[2]毛新军等．智能体的理论研究[J]．计算机科学，1997,24(5)：63-67．
[3]曹先彬，罗文坚，王煦法．基于生态种群竞争模型的协同进化[J]．软件学报，2001，12(4)：556562
[4]谭跃进，邓宏钟．复杂适应系统理论及其应用研究[J]．系统工程，2001，19(5)：1-6
[5]张守一，SWARM及其在经济研究中的应用[J]．数量经济技术经济研究。2001，18(1)：9497
[6]王小平，曹立明．遗传算法——理论、应用与软件实现[M]，西安：西安交通大学出版社，2002．1
[7]李宏亮，程华等．复杂自适应系统的描述及其分布仿真框架[A]，计算机研究与发展，2002， 39(10)：1349-1354
[8]胡代平，王洗尘．建立支持宏观经济决策研讨厅的预测模型系统[J]．系统工程学报，2001，16(5)：335-339
[9]冯珊，唐超，阂君，沈冲．用于复杂系统建模与仿真的面向智能体技术[J]．管理科学学报，1999， 2(2)：717
[10] 邓宏钟，谭跃进，迟妍。一种复杂系统研究方法——基于多智能体的整体建模仿真方法[J]。系统工程．2000，18(4)：73-7
[11]邓宏钟，谭跃进．基于复杂适应系统的自动建模方法研究，计算机仿真，2002，19(6)
[12]杨敏，邱菀华．复杂决策系统研究——框架及其方法．系统工程理论与实践,2002,9(9)
[13]王迪兴，复杂适应系统的共性描述．系统辩证学学报．2001，9(4)
[14]史忠植．智能主体及其应用[M]．北京：科学出版社
[15]许国志主编．系统科学．上海：上海科技教育出版社，2000，9
[16]范玉顺，曹军威编著．复杂系统的面向对象建模、分析与设计，北京：清华大学出版社，2000
[17]唐锡晋．模型集成．系统工程学报，2001，16(5)：322-328
[18]张永安，白学志．复杂系统的重要研究工具——细胞自动机及其应用[J]．自然杂志，1998,20(4)：192-196
[19]应尚军，魏一鸣，范英等．基于元胞自动机的股票市场投资行为模拟，系统工程学报，2001，16(5)
[20]丁浩，杨小平．SWARM—一个支持人工生命建模的面向对象模拟平台．系统仿真学报，2002，14(5):569-572
[26] Bayard R J, Info S Leuth. Agent-based semantic integration of information in open and dynamic environments[A]. Proceeding of the ACM SIGMOD Conference on Management of Data, 1997,195-205
[27] Steven F. Railsback. Concepts from complex adaptive systems as a framework for individual-based modeling. Ecological Modelling, 2001, 139:47-62
[28] Adam L.omnicki. Individual-based models and the individual-based approach to population ecology. Ecological Modelling, 1999,115:191-198
[29] S. Y. Auyang. Foundations of Complex-System Theories in Economics, Evolutionary Biology, and Statistical Physics. Cambridge, England: Cambridge University Press, 1998
[30] Gregg Hartvigsen, Ann Kinzig, Garry Peterson. Use and Analysis of Complex Adaptive Systems in Ecosystem Science. Ecosystems, 1998) 1:427-430
[31] Roger Burkhart. The swarm : multi-agent simulation system[C]. Position Paper for OOPSLA'94 Workshop on "The Object Engine", 1994
[32] MINAR, M., Burkhart, R., Langton, C, Askenazy, M. 1996, The Swarm Simulation System: AToolkit for Building Multi-agent Simulations. Santa Fe Institute.
[33]Benedikt Stefansson. Swarm- An Object Oriented Simulation Platform Applied to Markets and Organizations[R]. Evolutionary Programming VI Lecture Notes in Computer Science Vol. 1213.Springer-Verlag, NY, 1997
[34]Pietro Terna. Simulation Tools for Social Scientists: Building Agent Based Models with SWARM. Journal of Artificial Societies and Social Simulation, 1998,1(1)
[1] 王土同．模糊专家系统、模糊神经网络及其程序设计．上海：上海科技出版社，1998
[2] 刘康平，朱海萍．告警关联与故障诊断专家系统研究与实现．计算机工程，2002，28(6)：11-18
[3] 蔡自兴，徐光祜．人工智能及其应用．北京：清华大学出版社，2003
[4] 孔繁胜．知识库系统原理．杭州：浙江大学出版社，2000
[5] 杨炳儒，申京涛．基于知识发现的广义诊断型专家系统GDESKD新构造．模式识别与人工智能，1999．12(4)：373～380．
[6] Nils J N.Artificail Intelligence:A New Synthesis[M].Morgan Kaufmann Publishers Inc.1998
[7] Hori K. An Ontology of Strategic Knowledge : Key Concepts and Applications. Knowledge-based Systems, 2000, (13): 369-374
[8] Ishino Y, Hori K, Nakasuka S. Concept Development of Consumer Goods Utilizing Strategic Knowledge. Knowledge-based Systems, 2000, (13): 417-427.
[9] Karp P D.,Wilkins D C. An analysis of the distinction between deep and shallow expert systems. International Journal of Expert Systems, 1989,2(1): 1～32
[10] Joseph G.Gary R.Expert systems:Principles and Programming.second edition.Boston:PWS Publishing Company, 1993
[11] Daniel Z.,Shawn P. Fish disease diagnosis program-problems and some solutions.Aquacultural Engineering ,2000 , (23) :3 — 111
[12] Harmon P., Maus R. Morrissey W. Expert Systems Tools and Applications[M]. New York: Wiley, 1988.14-20.
[13] Russell S. .Norvig, P. Artificial Intelligence: A Modern Approach, Prentice Hall, New Jersey, 1995
[14] Lyndon M. Tan C, An object-oriented knowledge base for multi-domain expert systems[J].Expert System with Application, 1995, Vol. 8, No. 1,177-185
[15] Kyung H. Object-oriented approach to a knowledge-based structural design system[J].Expert System with Application, 1996, Vol.10,No.2,223-231
[16] Harmon P. Expert system building tools for object-oriented development[J].Journal of Object-Oriented Programming, 1991, Vol. 4, No. 3, 61-63
[17] Carver,S.J.,Integrating multi-criteria evaluation with GIS,International journal of geographical information systems, 1991 (5)
[18]Malczewski,J.,A GIS-based approach to multiple criteria group decision-making.lnternational journal of geographic information systems, 1996(10)
[19]Zhao Gengxing,Li Yuhuan.Li Qing.Quantitative and automatic evaluation method supported by GIS of agricultural land.Transaction of the CASE, 1999,15(1)
[20] Joseph Giarratano, Gary Riley. Expert Systems: Principles and Programming. 2nd edition. USA: PWS Publishing Company. 1993.
[21] Zlatareva, N.P. A Refinement Framework to Support Validation and Maintenance of Knowledge-based Systems. Expert Systems with Application. 1998,15: 245-252.
[22]Fayyad, U. Data Mining and Knowledge Discovery: Making Sense out of Data. IEEE Expert,1996,11(5): 20-25.
[23]J.Han,Y.Cai, N.Cercone. Knowledge Discovery in Databases- An Attribute Oriented Approach. In Proc, of the VLDB Conference, pp.547~559, Vancouver, British Columbia, Canada, 1992.
[24] Yang Bing-ru, Shen Jiang-tao. KDD* Based on Double-Base Cooperating Mecha-nism and Its Realization of Software. Journal of System Engineering and Electronics, 1999,10(4): 1-9.
[25] Yang Bing-ru. KD(D&K) And Double Bases Cooperating Mechanism. Journal of System Engineering and Electronics, 1999,10(2): 48-54.
[26] Yang Bing-ru, Liu Fa-sheng, Shen Jiang-tao. ESDK- A New Structure of Expert System Based on Knowledge Discovery. Journal of University of Science and Technology Beijing, 2000,7(1): 63-67.
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