面向瓦斯突出预测的人工免疫算法与模型研究
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摘要
煤与瓦斯突出是制约煤矿安全生产的严重自然灾害之一。鉴于目前其影响因素众多而突出机理尚未彻底研究清楚、突出灾害却日益加剧的现实状况,本文研究了免疫算法及网络模型在瓦斯突出预测中的应用。免疫算法是抽取了生物免疫系统独有的自适应、自组织、多样性、免疫记忆等进化学习机理形成的新信息处理技术,是计算智能领域继人工神经网络和进化计算之后的又一个研究热点。
应用关联规则挖掘技术对瓦斯监测数据进行分析,可以找出瓦斯监测数据与其影响因素之间的关联特性,从而确定影响煤与瓦斯突出的主要因素。基于瓦斯监测这类大型数据库,关联规则挖掘需要在挖掘效率、可用性、精确性等方面得到提升,提出了浓度抑制克隆选择算法CRCSA。CRCSA将浓度抑制和二次应答引入克隆选择算法,同时引用记忆机制,将抗体分为记忆单元和普通单元,对记忆单元和普通单元采用不同的变异策略,提高其向最优解的进化速度,加入随机产生和经过过度变异的抗体,以保持抗体种群的多样性。将CRCSA应用到关联规则挖掘中,设计了基于CRCSA的关联规则挖掘算法,并应用到煤与瓦斯突出预测中。
针对aiNET网络模型对抑制阈值非常敏感的缺陷,设计了距离浓度自适应aiNET网络模型DCAaiNET,并对DCAaiNET模型的网络学习算法进行了收敛性分析。
DCAaiNET模型采用基于距离浓度的抗体选择和抑制机制,以便在鼓励适应度高抗体的同时,限制浓度较高的抗体,增强群体的多样性,并减少参数特别是抑制阈值对模型的影响。从而使DCAaiNET模型达到降低计算成本,加快模型的收敛速度,减少人为主观因素的影响。将DCAaiNET模型用于聚类分析,算法的平均运行时间最短,平均迭代次数最少,且聚类的准确率最高。
提出了混沌免疫优化算法CIOA。CIOA结合了免疫算法和混沌优化算法各自的空间搜索优势,通过混沌变异算子引入新的“变种”,通过免疫选择算子实现“优胜劣汰”。算法能保持较好的多样性,同时具有较高的收敛速度及避免陷入局部最优。应用CIOA算法对RBF网络进行优化,能够对非线性时间序列进行有效的预测,且预测精度较传统的预测模型有一定的提高。最后将其应用于瓦斯突出的非线性时间序列预测,并且取得满意的效果。
开发了基于人工免疫的瓦斯突出预测原型系统。将瓦斯突出预测原型系统进行实际应用,通过对井下工作面瓦斯涌出量相关影响因素的聚类和关联规则挖掘,发现埋藏深度、构造煤厚、瓦斯含量等因素对瓦斯突出的影响;通过构造用于瓦斯涌出量时间序列预测的RBF网络,实现了对瓦斯涌出量的预测。预测结果与现场实际吻合较好。
As a massively parallel intelligent message process system, artificial immune system (AIS) has offered the new ideas for real-time project problem settlement. Immune algorithm is a new information processing technology, which collects unique evolution learning mechanism of biological immune system, such as adaptability, self-organization, diversity, immune memory, evolve, etc. And AIS has been a hot topic in computational intelligence after artificial neural network (ANN) and evolutionary computation nowadays. According to the demands of coal and gas outburst forecasting, the research in this dissertation is oriented on immune algorithm and immune network model and their application to coal and gas outburst prediction.
Meeting gas monitoring large-scale database, some aspects of association rules mining algorithm need to be improved mining efficiency, availability, accuracy and so on. A concentration restraining clonal selection algorithm (CRCSA) is proposed. CRCSA obtains inspiration from concentration restraining principle and secondary response of immune system, which introduces immune memory to divide the antibody into the memory unit and ordinary unit and adopt different variation tactics to the memory unit and ordinary antibody. Through joining random antibody and mutation antibody, CRCSA can keep antibody diversity and ensure the quick convergence and the optimal solution. CRCSA association rules mining method is put forward and is applied to coal and gas outburst forecasting. The results show that CRCSA association rules mining method is effective in practice.
Aimed at the sensitive restraining threshold shortcoming of artificial immune network (aiNET), distance concentration adaptive artificial immune network (DCAaiNET) is given in this dissertation, and network learning algorithm convergence of DCAaiNET is proved. DCAaiNET model adopts antibody selection and restraining mechanism based on distance concentration, which can limit the antibody of higher concentration and strengthen antibody diversity and reduce some parameters influence to DCAaiNET especially restraining threshold. Then cut down calculation cost and accelerate convergence speed and reduce subjective factors influence of DCAaiNET. DCAaiNET is applied to clustering analysis, and average running time of clustering algorithm is the shortest and iteration steps is the least and clustering accuracy is the highest. Using DCAaiNET clustering method in coal and gas emission prediction, satisfied results are achieved and the DCAaiNET clustering method is good.
Based on analysis of the basic principle and features of immune algorithm, this dissertation integrates respectively the space search advantages of immune algorithm and chaos optimization algorithm and presents a chaotic immune optimization algorithm (CIOA). By applying chaos mutation operator to producing new antibody and applying immune selection operator to realizing“the survival of the fittest”, CIOA is able to maintain a good diversity. At the same time, CIOA has higher convergence speed and it can avoid falling into local optima effectively. CIOA is used to optimize radial basis function neural network (RBFNN) which be effective for nonlinear time series prediction and prediction accuracy than conventional forecasting models have some improvement. Finally, the CIOA RBFNN is applied to coal and gas outburst forecasting and the result is satisfied.
At last, gas outburst forecasting prototype system based on AIS is developed. In real coal and gas outburst forecasting application, through clustering analysis and association rules mining of underground work face gas gushing, seam’s depth, coal construction thickness, gas content and other factors on the impact of gas outburst is found. By constructing RBFNN for gas emission quantity time series prediction, gas gushing prediction is realized, and the prediction result is identical with the fact.
应用关联规则挖掘技术对瓦斯监测数据进行分析,可以找出瓦斯监测数据与其影响因素之间的关联特性,从而确定影响煤与瓦斯突出的主要因素。基于瓦斯监测这类大型数据库,关联规则挖掘需要在挖掘效率、可用性、精确性等方面得到提升,提出了浓度抑制克隆选择算法CRCSA。CRCSA将浓度抑制和二次应答引入克隆选择算法,同时引用记忆机制,将抗体分为记忆单元和普通单元,对记忆单元和普通单元采用不同的变异策略,提高其向最优解的进化速度,加入随机产生和经过过度变异的抗体,以保持抗体种群的多样性。将CRCSA应用到关联规则挖掘中,设计了基于CRCSA的关联规则挖掘算法,并应用到煤与瓦斯突出预测中。
针对aiNET网络模型对抑制阈值非常敏感的缺陷,设计了距离浓度自适应aiNET网络模型DCAaiNET,并对DCAaiNET模型的网络学习算法进行了收敛性分析。
DCAaiNET模型采用基于距离浓度的抗体选择和抑制机制,以便在鼓励适应度高抗体的同时,限制浓度较高的抗体,增强群体的多样性,并减少参数特别是抑制阈值对模型的影响。从而使DCAaiNET模型达到降低计算成本,加快模型的收敛速度,减少人为主观因素的影响。将DCAaiNET模型用于聚类分析,算法的平均运行时间最短,平均迭代次数最少,且聚类的准确率最高。
提出了混沌免疫优化算法CIOA。CIOA结合了免疫算法和混沌优化算法各自的空间搜索优势,通过混沌变异算子引入新的“变种”,通过免疫选择算子实现“优胜劣汰”。算法能保持较好的多样性,同时具有较高的收敛速度及避免陷入局部最优。应用CIOA算法对RBF网络进行优化,能够对非线性时间序列进行有效的预测,且预测精度较传统的预测模型有一定的提高。最后将其应用于瓦斯突出的非线性时间序列预测,并且取得满意的效果。
开发了基于人工免疫的瓦斯突出预测原型系统。将瓦斯突出预测原型系统进行实际应用,通过对井下工作面瓦斯涌出量相关影响因素的聚类和关联规则挖掘,发现埋藏深度、构造煤厚、瓦斯含量等因素对瓦斯突出的影响;通过构造用于瓦斯涌出量时间序列预测的RBF网络,实现了对瓦斯涌出量的预测。预测结果与现场实际吻合较好。
As a massively parallel intelligent message process system, artificial immune system (AIS) has offered the new ideas for real-time project problem settlement. Immune algorithm is a new information processing technology, which collects unique evolution learning mechanism of biological immune system, such as adaptability, self-organization, diversity, immune memory, evolve, etc. And AIS has been a hot topic in computational intelligence after artificial neural network (ANN) and evolutionary computation nowadays. According to the demands of coal and gas outburst forecasting, the research in this dissertation is oriented on immune algorithm and immune network model and their application to coal and gas outburst prediction.
Meeting gas monitoring large-scale database, some aspects of association rules mining algorithm need to be improved mining efficiency, availability, accuracy and so on. A concentration restraining clonal selection algorithm (CRCSA) is proposed. CRCSA obtains inspiration from concentration restraining principle and secondary response of immune system, which introduces immune memory to divide the antibody into the memory unit and ordinary unit and adopt different variation tactics to the memory unit and ordinary antibody. Through joining random antibody and mutation antibody, CRCSA can keep antibody diversity and ensure the quick convergence and the optimal solution. CRCSA association rules mining method is put forward and is applied to coal and gas outburst forecasting. The results show that CRCSA association rules mining method is effective in practice.
Aimed at the sensitive restraining threshold shortcoming of artificial immune network (aiNET), distance concentration adaptive artificial immune network (DCAaiNET) is given in this dissertation, and network learning algorithm convergence of DCAaiNET is proved. DCAaiNET model adopts antibody selection and restraining mechanism based on distance concentration, which can limit the antibody of higher concentration and strengthen antibody diversity and reduce some parameters influence to DCAaiNET especially restraining threshold. Then cut down calculation cost and accelerate convergence speed and reduce subjective factors influence of DCAaiNET. DCAaiNET is applied to clustering analysis, and average running time of clustering algorithm is the shortest and iteration steps is the least and clustering accuracy is the highest. Using DCAaiNET clustering method in coal and gas emission prediction, satisfied results are achieved and the DCAaiNET clustering method is good.
Based on analysis of the basic principle and features of immune algorithm, this dissertation integrates respectively the space search advantages of immune algorithm and chaos optimization algorithm and presents a chaotic immune optimization algorithm (CIOA). By applying chaos mutation operator to producing new antibody and applying immune selection operator to realizing“the survival of the fittest”, CIOA is able to maintain a good diversity. At the same time, CIOA has higher convergence speed and it can avoid falling into local optima effectively. CIOA is used to optimize radial basis function neural network (RBFNN) which be effective for nonlinear time series prediction and prediction accuracy than conventional forecasting models have some improvement. Finally, the CIOA RBFNN is applied to coal and gas outburst forecasting and the result is satisfied.
At last, gas outburst forecasting prototype system based on AIS is developed. In real coal and gas outburst forecasting application, through clustering analysis and association rules mining of underground work face gas gushing, seam’s depth, coal construction thickness, gas content and other factors on the impact of gas outburst is found. By constructing RBFNN for gas emission quantity time series prediction, gas gushing prediction is realized, and the prediction result is identical with the fact.
引文
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