基于支持向量机的建模方法及其在材料加工中的应用研究
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摘要
众所周知,热加工过程是非线性、多变量、时变、强耦合的,并且涉及大量的不确定因素,因而该过程是典型的复杂过程。对热加工过程采用经典的建模方法获取其精确的数学模型是极为困难的。获取热加工过程中的知识模型,有助于认识复杂热加工过程的规律,获取人类智能的经验知识,甚至升华人类智能的经验知识,从而实现复杂热加工过程的自动化和智能化,所以获取热加工过程的知识模型具有重要意义。
近年来,用模糊集方法、神经网络方法、粗糙集方法以及混合方法获取热加工过程的知识模型成为科研人员关注的焦点,并取得了许多有意义的成果,但是这些方法仍存在不足,不能完全满足实际需要,有必要对这类复杂过程的建模方法做进一步的研究。
本文以支持向量机理论和模糊集理论为基础,针对热加工领域知识建模的复杂性,提出了基于支持向量机的模糊规则获取系统(Support Vector Machine-based Fuzzy Rules Discovery System, SVM-FRDS)和权重支持向量回归在线学习(C-weighted On-line Support Vector Regression, COSVR )建模方法,并对建模算法进行了深入的研究,利用Mackey-Glass混沌时间序列进行预测实验。试验证明SVM-FRDS模型具有良好的可理解性和满足要求的精度,COSVR方法获得模型能更好的反映模型的变化。并将SVM-FRDS和COSVR应用于GTAW焊接过程建模与控制、氧势法碳势影响因素分析以及修正模型的建立,验证了SVM-FRDS和COSVR在热加工领域的有效性。
本文主要研究内容如下:
1)“可理解性”是可靠系统的必备特性,特别由于热加工行业的复杂性,如果模型是可理解的,甚至是可修改的,模型的可靠度,适用性都将有所改善。本文讨论了一种新的基于支持向量机的模糊规则获取系统(SVM-FRDS)。支持向量机抽取支持向量的特点提供了从训练数据产生模糊规则的机制。在支持向量机抽取模糊规则的基础上,采用模糊基函数做为推理系统,利用梯度算法对模糊规则和模糊基函数推理系统进行自适应学习。在给出SVM-FRDS主要算法的基础上,从规则抽取和推理系统两方面与其他典型模糊规则获取系统做了对比分析。最后,使用Mackey-Glass混沌时间序列进行预测实验。实验结果表明,本文提出的SVM-FRDS在精度和可理解性方面(包括规则条数和推理系统)都有优势。
2)由于影响因素多,甚至不可控的干扰因素都很多,热加工行业中模型时变特性非常明显。本文在标准支持向量回归在线学习方法的基础上,研究了权重支持向量回归在线学习方法(COSVR),强化新样本对模型的修改,弱化历史样本的影响。使用基准数据Mackey-Glass混沌序列做了相关验证实验,试验结果表明本文提出的COSVR更能反映模型的变化。
3)将本文提出的SVM-FRDS和COSVR方法应用于铝合金脉冲GTAW焊接动态过程建模与控制:1)运用本文提出的SVM-FRDS和COSVR方法获取铝合金脉冲GTAW焊接动态过程知识模型。实验结果表明SVM-FRDS知识建模方法可以有效的获取铝合金脉冲GTAW焊接动态过程的规则性模型,模型的复杂程度和精度都是可以满足要求的,模型是易于理解的。COSVR实验结果表明,COSVR在焊接过程建模中更能反映模型的改变。2)根据焊接过程特点,提出了基于SVM-FRDS的自适应逆控制,并将其应用于GTAW焊接过程的控制,该方法只需要获得焊接过程的输入输出数据就可以自动抽取易于理解的控制规则,实现对系统的控制,最后通过工艺实验验证了控制器对铝合金脉冲GTAW焊缝成形的良好控制。
4)氧势法是应用广泛的碳势测量技术,但氧势法测量值与碳势实际值之间存在偏差,研究氧势法测量碳势的修正模型非常重要。论文先根据人工经验,测量并记录了相关数据,然后运用SVM方法分析了碳势的影响因素并建立了单因素和多因素修正模型。在知识模型的引导下,在碳势控制相关理论的基础上,建立了碳势修正模型的机理模型。最后运用本文提出的COSVR方法建立了碳势修正知识模型,提高了修正模型的精度,该方法的应用为修正模型的实际应用提供了重要保障。
5)设计并实现了基于权重支持向量回归在线学习的热加工知识获取系统(COSVRKDSHW),系统涵盖了支持向量回归及权重支持向量回归在线学习建模过程中的全部所需功能,并集成了一些辅助功能。
It is well known that heat processes are nonlinear, multivariable, timevarying and strong coupled. Thus, the heat processes are typical complex processes. It is too hard to obtain accurate mathematical models of hot work processes using classical modeling methods. Obtaining knowledge models of hot working processes is helpful for understanding the processes, obtaining and even abstracting the experiential knowledge of human and realizing the intelligent control of these processes. Thus, it is of great significance to obtain knowledge models of hot processing of metal.
In recent years, it is becoming the focus to get knowledge models of hot work processes with the fuzzy set method, the neural network method, the rough set methods and their combination. Many interesting results have been obtained, however, they could not completely satisfy the practical needs. Further modeling techniques are necessary.
Considering the complexities of hot working process, this dissertation proposes Support Vector Machine-based Fuzzy Rules Discovery System (SVM-FRDS) and C-weighted On-line support vector regression (COSVR) kowledge modeling method of complex processes based on the SVM and fuzzy system theory. Key algorithms of the method are studied in detail. Verifying experiments using chaotic Mackey–Glass are carried out. The experimental results show that the SVM-FRDS model possesses good generalization capability as well as high comprehensibility, the COSVR method can change the model more effectlively. The proposed approaches are applied in the heat processes. The proposed SVM-FRDS and COSVR methods are applied in modeling and control for the aluminum alloy pulse GTAW welding dynamic process. For controlling of carbon potential using an oxygen sensor we analyse the influencing factors and study the amendment model.The experimental results prove that the SVM-FRDS and COSVR method can effectively obtain the knowledge model in the field.
The main work is as follows:
1. In general, comprehensibility is one of the required characteristics of reliable systems. Especially in the complex hot work field, if the model is comprehensive, or even revisable, the model may be more reliable and adaptive. This paper discusses a support vector machine-based Fuzzy Rules Discovery System (SVM-FRDS). The character of SVM in extracting support vector provides a mechanism to extract fuzzy IF–THEN rules from the training data set. We construct the fuzzy inference system using fuzzy basis function. The gradient technique is used to tune the fuzzy rules and the inference system. Main algorithm of SVM-FRDS is given. We theoretically analyze the proposed SVM-FRDS on the rule extraction and the inference method comparing with other fuzzy systems. Comparative tests are performed using chaotic Mackey–Glass benchmark data. Comparative analysis and tests about SVM-FRDS with respect to other fuzzy systems show that the new approach possesses satisfactory generalization capability as well as high comprehensibility.
2. There are many influencing factors, even uncontrollable factors in the hot work processes, so the time-varying model is more acceptable. Based on the on-line support vector reression, this paper investigates C-weighted On-line Support Vector Reression approach (COSVR). In COSVR parameter C varies for different samples. This approach efficiently updates the trained function whenever a sample is added to the training set. Comparative tests are performed using chaotic Mackey–Glass benchmark. The experimental results show that the method can change the model more effectlively.
3. The proposed SVM-FRDS and COSVR methods are applied in modeling and control for the aluminum alloy pulse GTAW welding dynamic process. 1) Verifying experiments on obtaining knowledge models for GTAW welding process using the proposed SVM-FRDS and COSVR modeling method are carried out. For SVM-FRDS, the model possesses good generalization capability as well as high comprehensibility, and the SVM-FRDS adaptive inverse control method is feasible in welding process control. For COSVR, the method can change the model more effectively. 2) A new adaptive inverse control method based on SVM-FRDS is proposed and applied in GTAW process control. The proposed adaptive inverse control method can automatically extract control rule from the weld process data. The welding experiments results show that the SVM-FRDS adaptive inverse control method can achieve uniform weld formation during the pulsed GTAW welding.
4.The oxygen sensor is widely used in measuring the carbon potential (CP). There exists a deviation between the real CP value and the measured CP value using an oxygen sensor. It is very important to study the amendment model for controlling of CP using an oxygen sensor. We select the relevant variables according to the practical experience. We measure and record the relevant data according to the practical experience. We analyse the influencing factors and build sinle-variable and multi-variable models using the SVM approach. Under the guidance of the knowledge model, based on the carbon potential relevant theory, we build the mechanics model. We build the knowledge model using the proposed COSVR, and the model has higher precision. The COSVR modeling approach builds an important foundation for the practical application of amendment model.
5. C-weighted On-line Support Vector Regression Based Knowledge Discovery System for Hot Working (COSVRKDSHW) is designed and developed. The software system includes all functions needed by on-line support vector regression and c-weighted on-line support vector regression modeling method and integrates some auxiliary functions.
近年来,用模糊集方法、神经网络方法、粗糙集方法以及混合方法获取热加工过程的知识模型成为科研人员关注的焦点,并取得了许多有意义的成果,但是这些方法仍存在不足,不能完全满足实际需要,有必要对这类复杂过程的建模方法做进一步的研究。
本文以支持向量机理论和模糊集理论为基础,针对热加工领域知识建模的复杂性,提出了基于支持向量机的模糊规则获取系统(Support Vector Machine-based Fuzzy Rules Discovery System, SVM-FRDS)和权重支持向量回归在线学习(C-weighted On-line Support Vector Regression, COSVR )建模方法,并对建模算法进行了深入的研究,利用Mackey-Glass混沌时间序列进行预测实验。试验证明SVM-FRDS模型具有良好的可理解性和满足要求的精度,COSVR方法获得模型能更好的反映模型的变化。并将SVM-FRDS和COSVR应用于GTAW焊接过程建模与控制、氧势法碳势影响因素分析以及修正模型的建立,验证了SVM-FRDS和COSVR在热加工领域的有效性。
本文主要研究内容如下:
1)“可理解性”是可靠系统的必备特性,特别由于热加工行业的复杂性,如果模型是可理解的,甚至是可修改的,模型的可靠度,适用性都将有所改善。本文讨论了一种新的基于支持向量机的模糊规则获取系统(SVM-FRDS)。支持向量机抽取支持向量的特点提供了从训练数据产生模糊规则的机制。在支持向量机抽取模糊规则的基础上,采用模糊基函数做为推理系统,利用梯度算法对模糊规则和模糊基函数推理系统进行自适应学习。在给出SVM-FRDS主要算法的基础上,从规则抽取和推理系统两方面与其他典型模糊规则获取系统做了对比分析。最后,使用Mackey-Glass混沌时间序列进行预测实验。实验结果表明,本文提出的SVM-FRDS在精度和可理解性方面(包括规则条数和推理系统)都有优势。
2)由于影响因素多,甚至不可控的干扰因素都很多,热加工行业中模型时变特性非常明显。本文在标准支持向量回归在线学习方法的基础上,研究了权重支持向量回归在线学习方法(COSVR),强化新样本对模型的修改,弱化历史样本的影响。使用基准数据Mackey-Glass混沌序列做了相关验证实验,试验结果表明本文提出的COSVR更能反映模型的变化。
3)将本文提出的SVM-FRDS和COSVR方法应用于铝合金脉冲GTAW焊接动态过程建模与控制:1)运用本文提出的SVM-FRDS和COSVR方法获取铝合金脉冲GTAW焊接动态过程知识模型。实验结果表明SVM-FRDS知识建模方法可以有效的获取铝合金脉冲GTAW焊接动态过程的规则性模型,模型的复杂程度和精度都是可以满足要求的,模型是易于理解的。COSVR实验结果表明,COSVR在焊接过程建模中更能反映模型的改变。2)根据焊接过程特点,提出了基于SVM-FRDS的自适应逆控制,并将其应用于GTAW焊接过程的控制,该方法只需要获得焊接过程的输入输出数据就可以自动抽取易于理解的控制规则,实现对系统的控制,最后通过工艺实验验证了控制器对铝合金脉冲GTAW焊缝成形的良好控制。
4)氧势法是应用广泛的碳势测量技术,但氧势法测量值与碳势实际值之间存在偏差,研究氧势法测量碳势的修正模型非常重要。论文先根据人工经验,测量并记录了相关数据,然后运用SVM方法分析了碳势的影响因素并建立了单因素和多因素修正模型。在知识模型的引导下,在碳势控制相关理论的基础上,建立了碳势修正模型的机理模型。最后运用本文提出的COSVR方法建立了碳势修正知识模型,提高了修正模型的精度,该方法的应用为修正模型的实际应用提供了重要保障。
5)设计并实现了基于权重支持向量回归在线学习的热加工知识获取系统(COSVRKDSHW),系统涵盖了支持向量回归及权重支持向量回归在线学习建模过程中的全部所需功能,并集成了一些辅助功能。
It is well known that heat processes are nonlinear, multivariable, timevarying and strong coupled. Thus, the heat processes are typical complex processes. It is too hard to obtain accurate mathematical models of hot work processes using classical modeling methods. Obtaining knowledge models of hot working processes is helpful for understanding the processes, obtaining and even abstracting the experiential knowledge of human and realizing the intelligent control of these processes. Thus, it is of great significance to obtain knowledge models of hot processing of metal.
In recent years, it is becoming the focus to get knowledge models of hot work processes with the fuzzy set method, the neural network method, the rough set methods and their combination. Many interesting results have been obtained, however, they could not completely satisfy the practical needs. Further modeling techniques are necessary.
Considering the complexities of hot working process, this dissertation proposes Support Vector Machine-based Fuzzy Rules Discovery System (SVM-FRDS) and C-weighted On-line support vector regression (COSVR) kowledge modeling method of complex processes based on the SVM and fuzzy system theory. Key algorithms of the method are studied in detail. Verifying experiments using chaotic Mackey–Glass are carried out. The experimental results show that the SVM-FRDS model possesses good generalization capability as well as high comprehensibility, the COSVR method can change the model more effectlively. The proposed approaches are applied in the heat processes. The proposed SVM-FRDS and COSVR methods are applied in modeling and control for the aluminum alloy pulse GTAW welding dynamic process. For controlling of carbon potential using an oxygen sensor we analyse the influencing factors and study the amendment model.The experimental results prove that the SVM-FRDS and COSVR method can effectively obtain the knowledge model in the field.
The main work is as follows:
1. In general, comprehensibility is one of the required characteristics of reliable systems. Especially in the complex hot work field, if the model is comprehensive, or even revisable, the model may be more reliable and adaptive. This paper discusses a support vector machine-based Fuzzy Rules Discovery System (SVM-FRDS). The character of SVM in extracting support vector provides a mechanism to extract fuzzy IF–THEN rules from the training data set. We construct the fuzzy inference system using fuzzy basis function. The gradient technique is used to tune the fuzzy rules and the inference system. Main algorithm of SVM-FRDS is given. We theoretically analyze the proposed SVM-FRDS on the rule extraction and the inference method comparing with other fuzzy systems. Comparative tests are performed using chaotic Mackey–Glass benchmark data. Comparative analysis and tests about SVM-FRDS with respect to other fuzzy systems show that the new approach possesses satisfactory generalization capability as well as high comprehensibility.
2. There are many influencing factors, even uncontrollable factors in the hot work processes, so the time-varying model is more acceptable. Based on the on-line support vector reression, this paper investigates C-weighted On-line Support Vector Reression approach (COSVR). In COSVR parameter C varies for different samples. This approach efficiently updates the trained function whenever a sample is added to the training set. Comparative tests are performed using chaotic Mackey–Glass benchmark. The experimental results show that the method can change the model more effectlively.
3. The proposed SVM-FRDS and COSVR methods are applied in modeling and control for the aluminum alloy pulse GTAW welding dynamic process. 1) Verifying experiments on obtaining knowledge models for GTAW welding process using the proposed SVM-FRDS and COSVR modeling method are carried out. For SVM-FRDS, the model possesses good generalization capability as well as high comprehensibility, and the SVM-FRDS adaptive inverse control method is feasible in welding process control. For COSVR, the method can change the model more effectively. 2) A new adaptive inverse control method based on SVM-FRDS is proposed and applied in GTAW process control. The proposed adaptive inverse control method can automatically extract control rule from the weld process data. The welding experiments results show that the SVM-FRDS adaptive inverse control method can achieve uniform weld formation during the pulsed GTAW welding.
4.The oxygen sensor is widely used in measuring the carbon potential (CP). There exists a deviation between the real CP value and the measured CP value using an oxygen sensor. It is very important to study the amendment model for controlling of CP using an oxygen sensor. We select the relevant variables according to the practical experience. We measure and record the relevant data according to the practical experience. We analyse the influencing factors and build sinle-variable and multi-variable models using the SVM approach. Under the guidance of the knowledge model, based on the carbon potential relevant theory, we build the mechanics model. We build the knowledge model using the proposed COSVR, and the model has higher precision. The COSVR modeling approach builds an important foundation for the practical application of amendment model.
5. C-weighted On-line Support Vector Regression Based Knowledge Discovery System for Hot Working (COSVRKDSHW) is designed and developed. The software system includes all functions needed by on-line support vector regression and c-weighted on-line support vector regression modeling method and integrates some auxiliary functions.
引文
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