基于模糊贝叶斯网络的食品安全控制知识推理模型的研究
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摘要
贝叶斯网络是随机不确定性推理的有效工具,然而在实际应用中,经常遇见模糊随机事物的不确定性推理问题。如预测明天“好天气”的可能性,明天天气“状态”是一随机事件,但“天气是好状态”是模糊事件。如何应用贝叶斯网络解决这一类混合不确定性的知识推理问题已成为研究热点。本文提出了“基于遗传算法的模糊贝叶斯网络建立算法”:首先采用模糊数学的原理,定义了混合事件及其概率,第一次提出了条件模糊概率表的概念,有效解决了同时具备模糊性和随机性的变量的问题表示,通过聚类寻找高斯隶属度的参数,用遗传算法优化结构学习与参数学习,根据推理的分类误差,隶属度误差等反馈寻找最优的网络结构,同时通过修正隶属度函数的参数同步修正网络参数,特别地对模糊概率定义的参数α进行了优化确定,进而建立起模糊贝叶斯网络。
针对当前食品安全控制领域出现的难以进行事前风险诊断、安全预警、事后科学界定责任等实际问题,根据食品安全控制领域数据的特点,本文提出了建立基于模糊贝叶斯网络的食品安全风险知识推理模型:通过对广州市质监局的溯源系统的数据研究,提取了与食品安全风险有关的指标,以统计方法定义指标的取值,获取了样本数据,并用“基于遗传算法的模糊贝叶斯网络建立算法”建立起食品安全控制知识的推理与诊断模型。应用结果表明,基于遗传算法的模糊贝叶斯网络虽然因模糊数学的处理增加了计算复杂度和运行时间,但是由于其采用模糊逻辑,能直接反映食品生产过程某一环节“风险出现高”的可能性的模糊随机问题的推理与诊断,且与一般贝叶斯网络比较发现,模糊贝叶斯网络有着更高的推理正确率。
Bayesian network is an effective tool for uncertainty reasoning, but in practice, we often meet fuzzy stochastic uncertain reasoning problems. For example we want to forecast the possibility of tomorrow as "good weather" ,future weather "state" is a random event, but "the weather is good state" is a fuzzy event. How to use Bayesian network to solve this kind of mixed uncertainty knowledge inference has become a research hotspot. This paper proposes a novel approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm : first of all, according to the principle of fuzzy mathematics, define of a mixed event and its probability, define the conditions fuzzy probability table for the first time, effectively solves the problem of the denote of the random variable, find the parameters of the Gaussian membership by clustering, optimize structure learning and parameter learning by Genetic Algorithm, find the optimal network structure according to the classification error and membership error of reasoning, and modify network parameters by modifying the parameters of membership functions, in particular the parameterαin the definition of the fuzzy probability is optimized to determine, in the end set up a fuzzy Bayesian network.
View of the current food safety control is difficult to advance the field of risk diagnosis, early warning, scientific definition of responsibility after the real problems. After research the data of food safety control features in this field, this paper proposes reasoning model of control knowledge of food safety based on Fuzzy Bayesian Network: after the research of the data of Guangzhou Municipal Quality Supervision Bureau of the traceability system, extract the indicators related to food safety risk, define the value of this indicators by statistical method, obtain the sample data, and use the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm to establish reasoning model of control knowledge of food safety. The application results shows that the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm, increases the computational complexity and running time because of its use of fuzzy logic, but just using fuzzy logic can directly reflect the possibility of high food safe risk in the food production process in the reasoning and diagnosis. Compared with the general Bayesian network, fuzzy Bayesian network has a higher accuracy of inference.
针对当前食品安全控制领域出现的难以进行事前风险诊断、安全预警、事后科学界定责任等实际问题,根据食品安全控制领域数据的特点,本文提出了建立基于模糊贝叶斯网络的食品安全风险知识推理模型:通过对广州市质监局的溯源系统的数据研究,提取了与食品安全风险有关的指标,以统计方法定义指标的取值,获取了样本数据,并用“基于遗传算法的模糊贝叶斯网络建立算法”建立起食品安全控制知识的推理与诊断模型。应用结果表明,基于遗传算法的模糊贝叶斯网络虽然因模糊数学的处理增加了计算复杂度和运行时间,但是由于其采用模糊逻辑,能直接反映食品生产过程某一环节“风险出现高”的可能性的模糊随机问题的推理与诊断,且与一般贝叶斯网络比较发现,模糊贝叶斯网络有着更高的推理正确率。
Bayesian network is an effective tool for uncertainty reasoning, but in practice, we often meet fuzzy stochastic uncertain reasoning problems. For example we want to forecast the possibility of tomorrow as "good weather" ,future weather "state" is a random event, but "the weather is good state" is a fuzzy event. How to use Bayesian network to solve this kind of mixed uncertainty knowledge inference has become a research hotspot. This paper proposes a novel approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm : first of all, according to the principle of fuzzy mathematics, define of a mixed event and its probability, define the conditions fuzzy probability table for the first time, effectively solves the problem of the denote of the random variable, find the parameters of the Gaussian membership by clustering, optimize structure learning and parameter learning by Genetic Algorithm, find the optimal network structure according to the classification error and membership error of reasoning, and modify network parameters by modifying the parameters of membership functions, in particular the parameterαin the definition of the fuzzy probability is optimized to determine, in the end set up a fuzzy Bayesian network.
View of the current food safety control is difficult to advance the field of risk diagnosis, early warning, scientific definition of responsibility after the real problems. After research the data of food safety control features in this field, this paper proposes reasoning model of control knowledge of food safety based on Fuzzy Bayesian Network: after the research of the data of Guangzhou Municipal Quality Supervision Bureau of the traceability system, extract the indicators related to food safety risk, define the value of this indicators by statistical method, obtain the sample data, and use the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm to establish reasoning model of control knowledge of food safety. The application results shows that the approach of Fuzzy Bayesian Network Construction which is based on Genetic Algorithm, increases the computational complexity and running time because of its use of fuzzy logic, but just using fuzzy logic can directly reflect the possibility of high food safe risk in the food production process in the reasoning and diagnosis. Compared with the general Bayesian network, fuzzy Bayesian network has a higher accuracy of inference.
引文
[1]梁之舜,邓集贤,杨维权,司徒荣,邓永录,概率论与数理统计,高等教育出版社,1988,1-64
[2]汪德宁,模糊推理在心电诊断模糊专家系统中的研究与实现,国防科学技术大学:国防科学技术大学研究生院,2004,13-14
[3] Libor Běhounek, On the difference between traditional and deductive fuzzy logic, Fuzzy Sets and Systems, Volume 159, Issue 10, 16 May 2008, Pages 1153-1164
[4] Ond?ej Pavla?ka, Jana Tala?ová, Fuzzy vectors as a tool for modeling uncertain multidimensional quantities, Fuzzy Sets and Systems, Volume 161, Issue 11, 1 June 2010, Pages 1585-1603
[5] Xiaofeng Liang, Witold Pedrycz, Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms, Fuzzy Sets and Systems, Volume 160, Issue 24, 16 December 2009, Pages 3475-3502
[6] Luciano Stefanini, A generalization of Hukuhara difference and division for interval and fuzzy arithmetic, Fuzzy Sets and Systems, Volume 161, Issue 11, 1 June 2010, Pages 1564-1584
[7] Martin ?těpni?ka, Ulrich Bodenhofer, Martina Daňková, Vilém Novák, Continuity issues of the implicational interpretation of fuzzy rules, Fuzzy Sets and Systems, Volume 161, Issue 14, 16 July 2010, Pages 1959-1972
[8] Tomá? Kroupa, Filters in fuzzy class theory, Fuzzy Sets and Systems, Volume 159, Issue 14, 16 July 2008, Pages 1773-1787
[9] Konstantinos A. Chrysafis, Basil K. Papadopoulos, G. Papaschinopoulos, On the fuzzy difference equations of finance, Fuzzy Sets and Systems, Volume 159, Issue 24, 16 December 2008, Pages 3259-3270
[10]杨莉,基于可能性理论的发电公司报价策略研究,浙江大学:浙江大学研究生院,2003,19-34
[11]佟欣,基于可能性理论的模糊可靠性设计,大连理工大学:大连理工大学研究生院,2004,25-26
[12]康长青,郭立红,罗艳春,王心醉,基于模糊贝叶斯网络的态势威胁评估模型,光电工程, 2008,35(5),2-3
[13]张连文、郭海鹏,贝叶斯网引论,科学出版社,2006,31-65
[14] Tomi Silander, Teemu Roos, Petri Myllym?ki, Learning locally minimax optimal Bayesian networks, International Journal of Approximate Reasoning, Volume 51, Issue 5, June 2010, Pages 544-557
[15] Franz Pernkopf, Tuan Van Pham, Jeff A. Bilmes, Broad phonetic classification using discriminative Bayesian networks, Speech Communication, Volume 51, Issue 2, February 2009, Pages 151-166
[16] Valentin Ziegler, Approximation algorithms for restricted Bayesian network structures, Information Processing Letters, Volume 108, Issue 2, 30 September 2008, Pages 60-63
[17] Olga Goubanova, Simon King, Bayesian networks for phone duration prediction, Speech Communication, Volume 50, Issue 4, April 2008, Pages 301-311
[18] C.J. Butz, S. Hua, J. Chen, H. Yao, A simple graphical approach for understanding probabilistic inference in Bayesian networks, Information Sciences, Volume 179, Issue 6, 1 March 2009, Pages 699-716
[19] C.H. Lo, Y.K. Wong, A.B. Rad, Bond graph based Bayesian network for fault diagnosis, Applied Soft Computing, In Press, Corrected Proof, Available online 6 March 2010
[20] Ana C.V. de Melo, Adilson J. Sanchez, Software maintenance project delays prediction using Bayesian Networks, Expert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 908-919
[21] Rubén Arma?anzas, I?aki Inza, Pedro Larra?aga, Detecting reliable gene interactions by a hierarchy ofBayesian network classifiers, Computer Methods and Programs in Biomedicine, Volume 91, Issue 2, August 2008, Pages 110-121
[22]华在峰,一种基于规则库的贝叶斯网络开发器的设计与实现,东北大学:东北大学研究生院,2005:26-48
[23]张薇薇,卢玉贞,孙建英,基于信息论和免疫遗传算法学习贝叶斯网络结构,大连海事大学学报,2006.8
[24]玄光男,程润伟,遗传算法与工程设计[M],科学出版社,2000,1-30
[25] Chung Hae Park, Woo Il Lee, Woo Suck Han, Alain Vautrin, Improved genetic algorithm for multidisciplinary optimization of composite laminates, Computers & Structures, Volume 86, Issues 19-20, October 2008, Pages 1894-1903
[26] Zvi Drezner, Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem, Computers & Operations Research, Volume 35, Issue 3, March 2008, Pages 717-736
[27] W. Paszkowicz, Properties of a genetic algorithm equipped with a dynamic penalty function, Computational Materials Science, Volume 45, Issue 1, March 2009, Pages 77-83
[28] Xiao-Ping Zeng, Yong-Ming Li, Jian Qin, A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection, Neurocomputing, Volume 72, Issues 4-6, January 2009, Pages 1214-1228
[29] Jinwei Gu, Manzhan Gu, Cuiwen Cao, Xingsheng Gu, A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem, Computers & Operations Research, Volume 37, Issue 5, May 2010, Pages 927-937
[30] Mehmet Aci, Cigdem ?nan, Mutlu Avci, A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm, Expert Systems with Applications, Volume 37, Issue 7, July 2010, Pages 5061-5067
[31] Yousef Kilani, Comparing the performance of the genetic and local search algorithms for solving the satisfiability problems, Applied Soft Computing, Volume 10, Issue 1, January 2010, Pages 198-207
[32]陈望宇,基于遗传算法的贝叶斯网络自适应知识建立与推理研究,华南理工大学,华南理工大学研究生院,2009,23-26
[33] Linden J. Ball, Bo T. Christensen, Analogical reasoning and mental simulation in design: two strategies linked to uncertainty resolution, Design Studies, Volume 30, Issue 2, March 2009, Pages 169-186
[34] Ying-Ming Wang, Taha M.S. Elhag, Evidential reasoning approach for bridge condition assessment, Expert Systems with Applications, Volume 34, Issue 1, January 2008, Pages 689-699
[35] E. Doménech, I. Escriche, S. Martorell, Assessing the effectiveness of critical control points to guarantee food safety, Food Control, Volume 19, Issue 6, June 2008, Pages 557-565
[36] Mojca Jev?nik, Silvestra Hoyer, Peter Raspor, Food safety knowledge and practices among pregnant and non-pregnant women in Slovenia, Food Control, Volume 19, Issue 5, May 2008, Pages 526-534
[37]陈宗道,赵国华,食品物流安全的管理与技术,化学工业出版社,2007,24-26
[38]陈宗道,刘金福,陈绍军,食品质量管理,中国农业大学出版社,2003,163-175
[39]合肥市质量技术监督局,广州食品企业推行食品安全主任制强化食品安全主体责任,http://hf.ahfs.gov.cn/article/200908/17022.html,2009-08-17
[40]广州市质量技术监督局,广州市质量技术监督局2010年工作计划,http://www.gzq.gov.cn/public/news.jsp?catid=26&id=19489,2010-03-05
[41]刘为军,中国食品安全控制研究,西北农林科技大学:西北农林科技大学研究生院,2006,16-29
[2]汪德宁,模糊推理在心电诊断模糊专家系统中的研究与实现,国防科学技术大学:国防科学技术大学研究生院,2004,13-14
[3] Libor Běhounek, On the difference between traditional and deductive fuzzy logic, Fuzzy Sets and Systems, Volume 159, Issue 10, 16 May 2008, Pages 1153-1164
[4] Ond?ej Pavla?ka, Jana Tala?ová, Fuzzy vectors as a tool for modeling uncertain multidimensional quantities, Fuzzy Sets and Systems, Volume 161, Issue 11, 1 June 2010, Pages 1585-1603
[5] Xiaofeng Liang, Witold Pedrycz, Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms, Fuzzy Sets and Systems, Volume 160, Issue 24, 16 December 2009, Pages 3475-3502
[6] Luciano Stefanini, A generalization of Hukuhara difference and division for interval and fuzzy arithmetic, Fuzzy Sets and Systems, Volume 161, Issue 11, 1 June 2010, Pages 1564-1584
[7] Martin ?těpni?ka, Ulrich Bodenhofer, Martina Daňková, Vilém Novák, Continuity issues of the implicational interpretation of fuzzy rules, Fuzzy Sets and Systems, Volume 161, Issue 14, 16 July 2010, Pages 1959-1972
[8] Tomá? Kroupa, Filters in fuzzy class theory, Fuzzy Sets and Systems, Volume 159, Issue 14, 16 July 2008, Pages 1773-1787
[9] Konstantinos A. Chrysafis, Basil K. Papadopoulos, G. Papaschinopoulos, On the fuzzy difference equations of finance, Fuzzy Sets and Systems, Volume 159, Issue 24, 16 December 2008, Pages 3259-3270
[10]杨莉,基于可能性理论的发电公司报价策略研究,浙江大学:浙江大学研究生院,2003,19-34
[11]佟欣,基于可能性理论的模糊可靠性设计,大连理工大学:大连理工大学研究生院,2004,25-26
[12]康长青,郭立红,罗艳春,王心醉,基于模糊贝叶斯网络的态势威胁评估模型,光电工程, 2008,35(5),2-3
[13]张连文、郭海鹏,贝叶斯网引论,科学出版社,2006,31-65
[14] Tomi Silander, Teemu Roos, Petri Myllym?ki, Learning locally minimax optimal Bayesian networks, International Journal of Approximate Reasoning, Volume 51, Issue 5, June 2010, Pages 544-557
[15] Franz Pernkopf, Tuan Van Pham, Jeff A. Bilmes, Broad phonetic classification using discriminative Bayesian networks, Speech Communication, Volume 51, Issue 2, February 2009, Pages 151-166
[16] Valentin Ziegler, Approximation algorithms for restricted Bayesian network structures, Information Processing Letters, Volume 108, Issue 2, 30 September 2008, Pages 60-63
[17] Olga Goubanova, Simon King, Bayesian networks for phone duration prediction, Speech Communication, Volume 50, Issue 4, April 2008, Pages 301-311
[18] C.J. Butz, S. Hua, J. Chen, H. Yao, A simple graphical approach for understanding probabilistic inference in Bayesian networks, Information Sciences, Volume 179, Issue 6, 1 March 2009, Pages 699-716
[19] C.H. Lo, Y.K. Wong, A.B. Rad, Bond graph based Bayesian network for fault diagnosis, Applied Soft Computing, In Press, Corrected Proof, Available online 6 March 2010
[20] Ana C.V. de Melo, Adilson J. Sanchez, Software maintenance project delays prediction using Bayesian Networks, Expert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 908-919
[21] Rubén Arma?anzas, I?aki Inza, Pedro Larra?aga, Detecting reliable gene interactions by a hierarchy ofBayesian network classifiers, Computer Methods and Programs in Biomedicine, Volume 91, Issue 2, August 2008, Pages 110-121
[22]华在峰,一种基于规则库的贝叶斯网络开发器的设计与实现,东北大学:东北大学研究生院,2005:26-48
[23]张薇薇,卢玉贞,孙建英,基于信息论和免疫遗传算法学习贝叶斯网络结构,大连海事大学学报,2006.8
[24]玄光男,程润伟,遗传算法与工程设计[M],科学出版社,2000,1-30
[25] Chung Hae Park, Woo Il Lee, Woo Suck Han, Alain Vautrin, Improved genetic algorithm for multidisciplinary optimization of composite laminates, Computers & Structures, Volume 86, Issues 19-20, October 2008, Pages 1894-1903
[26] Zvi Drezner, Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem, Computers & Operations Research, Volume 35, Issue 3, March 2008, Pages 717-736
[27] W. Paszkowicz, Properties of a genetic algorithm equipped with a dynamic penalty function, Computational Materials Science, Volume 45, Issue 1, March 2009, Pages 77-83
[28] Xiao-Ping Zeng, Yong-Ming Li, Jian Qin, A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection, Neurocomputing, Volume 72, Issues 4-6, January 2009, Pages 1214-1228
[29] Jinwei Gu, Manzhan Gu, Cuiwen Cao, Xingsheng Gu, A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem, Computers & Operations Research, Volume 37, Issue 5, May 2010, Pages 927-937
[30] Mehmet Aci, Cigdem ?nan, Mutlu Avci, A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm, Expert Systems with Applications, Volume 37, Issue 7, July 2010, Pages 5061-5067
[31] Yousef Kilani, Comparing the performance of the genetic and local search algorithms for solving the satisfiability problems, Applied Soft Computing, Volume 10, Issue 1, January 2010, Pages 198-207
[32]陈望宇,基于遗传算法的贝叶斯网络自适应知识建立与推理研究,华南理工大学,华南理工大学研究生院,2009,23-26
[33] Linden J. Ball, Bo T. Christensen, Analogical reasoning and mental simulation in design: two strategies linked to uncertainty resolution, Design Studies, Volume 30, Issue 2, March 2009, Pages 169-186
[34] Ying-Ming Wang, Taha M.S. Elhag, Evidential reasoning approach for bridge condition assessment, Expert Systems with Applications, Volume 34, Issue 1, January 2008, Pages 689-699
[35] E. Doménech, I. Escriche, S. Martorell, Assessing the effectiveness of critical control points to guarantee food safety, Food Control, Volume 19, Issue 6, June 2008, Pages 557-565
[36] Mojca Jev?nik, Silvestra Hoyer, Peter Raspor, Food safety knowledge and practices among pregnant and non-pregnant women in Slovenia, Food Control, Volume 19, Issue 5, May 2008, Pages 526-534
[37]陈宗道,赵国华,食品物流安全的管理与技术,化学工业出版社,2007,24-26
[38]陈宗道,刘金福,陈绍军,食品质量管理,中国农业大学出版社,2003,163-175
[39]合肥市质量技术监督局,广州食品企业推行食品安全主任制强化食品安全主体责任,http://hf.ahfs.gov.cn/article/200908/17022.html,2009-08-17
[40]广州市质量技术监督局,广州市质量技术监督局2010年工作计划,http://www.gzq.gov.cn/public/news.jsp?catid=26&id=19489,2010-03-05
[41]刘为军,中国食品安全控制研究,西北农林科技大学:西北农林科技大学研究生院,2006,16-29