基于支持向量机的模拟电路性能在线评价策略研究
发布时间：2019-07-05 09:42
【摘要】：针对传统的模拟电路性能评价方法对于错值处理的缺陷,以及较差的实时性,难以应用于在线评价,本文以支持向量回归机(LSSVR)为基础,研究了基于支持向量机的模拟电路性能在线评价方法。主要内容包括：在线聚类算法及子模型连接方法研究,鲁棒LSSVR、模糊LSSVR(FLSSVR)及增减交互模型在性能评价中的应用,粒子群算法优化核参数。具体工作如下：(1)提出基于聚类分析的FLSSVR在线模拟电路性能在线评价策略。该策略考虑支持向量机小样本依赖的局限性,采用模糊聚类算法将整个数据样本集根据数据特征划分为具有多个具有同类特征的子样本集,从而进行训练分析,有效降低了支持向量机对于小样本的依赖性。(2)提出基于鲁棒LSSVR的模拟电路性能评价新策略。本文运用标准LSSVR,结合鲁棒学习的优越性,设计修正多核径向基核函数在线调节核宽度保证支持向量数目确定的精确性,利用改进的鲁棒学习算法处理包含错值的数据集,在线完成模拟电路输出预测与实际输出对比,获取预测误差。该方法利用鲁棒学习算法更新LSSVR权值处理错值,同时应用增量、减量交互的学习方法兼顾历史数据,控制存储数据总量,完成RLSSVR模型的在线更新,并通过仿真实验验证了所提方法的可行性。(3)提出基于改进隶属度函数的FLSSVR模拟电路性能在线评价策略。该策略利用FLSSVR对每个样本根据其重要程度分别赋一个隶属度值,从而实现对错值和干扰的有效抑制。传统的基于距离的隶属度函数不能准确反映样本数据间的相互关系,容易遗漏异常样本,从而导致异常样本与正常样本具有相同的隶属度值。本文结合k近邻思想,对隶属度函数进行了修正。(4)研究FLSSVR参数优化及适用于模糊聚类算法的多模型连接方法。在线聚类算法虽能有效的解决支持向量机小样本的困扰,但同时欠缺有效的子模型连接方法,本文给出了应用于模拟电路性能评价策略的开关切换及加权组合子模型连接方法,并通过仿真实验验证了所提方法的有效性。
文内图片：
图片说明：图３－１两个不同类中样本么间紧密度的差别逡逑Ｆｉｇ．３－１邋Ｔｈｅ邋ｄｉｆｆｅｒｅｎｃｅ邋ｏｆ邋ｃｌｏｓｅ邋ｄｅｇｒｅｅ邋ｂｅｔｗｅｅｎ邋ｔｗｏ邋ｓａｍｐｌｅｓ邋ｉｎ邋ｄｉｆｆｅｒｅｎｔ邋ｃｌａｓｓ逡逑
[Abstract]:Based on the support vector regression machine (LSSVR), the on-line evaluation method of the performance of the analog circuit based on the support vector machine is studied. The main contents include: on-line clustering algorithm and sub-model connection method, robust LSSVR, fuzzy LSSVR (FLSSVR) and increase and decrease interaction model in performance evaluation, and particle swarm optimization. The specific work is as follows: (1) The online evaluation strategy of FLSSVR on-line simulation circuit based on cluster analysis is put forward. The strategy takes into account the limitation of support vector machine small sample dependency, and adopts the fuzzy clustering algorithm to divide the whole data sample set into a plurality of sub-sample sets with similar characteristics according to the data characteristics, so that the training analysis is carried out, and the dependence of the support vector machine on the small samples is effectively reduced. (2) A new strategy for evaluating the performance of an analog circuit based on robust LSSVR is presented. In this paper, we use the standard LSSVR to combine the advantages of robust learning, to design the modified multi-core radial basis function to adjust the kernel width on-line to guarantee the accuracy of the number of support vectors, and to use the improved robust learning algorithm to process the data set containing the error value. And the on-line completion of the analog circuit output prediction is compared with the actual output to obtain a prediction error. The method uses the robust learning algorithm to update the LSSVR weight value processing error value, and simultaneously applies the learning method of the increment and decrement interaction to balance the historical data, controls the total amount of the stored data, completes the on-line updating of the RLSSVR model, and verifies the feasibility of the proposed method through the simulation experiment. (3) An on-line evaluation strategy of FLSSVR analog circuit based on improved membership function is proposed. The strategy utilizes FLSSVR to assign a membership value to each sample according to their importance, so as to realize the effective suppression of the error value and the interference. The traditional distance-based membership function can not accurately reflect the relationship between the sample data and easily miss the abnormal sample, thus leading to the abnormal sample having the same membership value as the normal sample. In this paper, the membership function is modified with the idea of k-nearest neighbor. (4) The optimization of FLSSVR parameters and the multi-model connection method for fuzzy clustering algorithm are studied. Although the on-line clustering algorithm can effectively solve the problem of the small sample of the support vector machine, but at the same time the effective sub-model connection method is lacking, the invention provides a switch switching and weighting combination sub-model connection method applied to the performance evaluation strategy of the analog circuit, The validity of the proposed method is verified by the simulation experiment.
【学位授予单位】：渤海大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN710;TP18
，
本文编号：2510437
文内图片：
图片说明：图３－１两个不同类中样本么间紧密度的差别逡逑Ｆｉｇ．３－１邋Ｔｈｅ邋ｄｉｆｆｅｒｅｎｃｅ邋ｏｆ邋ｃｌｏｓｅ邋ｄｅｇｒｅｅ邋ｂｅｔｗｅｅｎ邋ｔｗｏ邋ｓａｍｐｌｅｓ邋ｉｎ邋ｄｉｆｆｅｒｅｎｔ邋ｃｌａｓｓ逡逑
[Abstract]:Based on the support vector regression machine (LSSVR), the on-line evaluation method of the performance of the analog circuit based on the support vector machine is studied. The main contents include: on-line clustering algorithm and sub-model connection method, robust LSSVR, fuzzy LSSVR (FLSSVR) and increase and decrease interaction model in performance evaluation, and particle swarm optimization. The specific work is as follows: (1) The online evaluation strategy of FLSSVR on-line simulation circuit based on cluster analysis is put forward. The strategy takes into account the limitation of support vector machine small sample dependency, and adopts the fuzzy clustering algorithm to divide the whole data sample set into a plurality of sub-sample sets with similar characteristics according to the data characteristics, so that the training analysis is carried out, and the dependence of the support vector machine on the small samples is effectively reduced. (2) A new strategy for evaluating the performance of an analog circuit based on robust LSSVR is presented. In this paper, we use the standard LSSVR to combine the advantages of robust learning, to design the modified multi-core radial basis function to adjust the kernel width on-line to guarantee the accuracy of the number of support vectors, and to use the improved robust learning algorithm to process the data set containing the error value. And the on-line completion of the analog circuit output prediction is compared with the actual output to obtain a prediction error. The method uses the robust learning algorithm to update the LSSVR weight value processing error value, and simultaneously applies the learning method of the increment and decrement interaction to balance the historical data, controls the total amount of the stored data, completes the on-line updating of the RLSSVR model, and verifies the feasibility of the proposed method through the simulation experiment. (3) An on-line evaluation strategy of FLSSVR analog circuit based on improved membership function is proposed. The strategy utilizes FLSSVR to assign a membership value to each sample according to their importance, so as to realize the effective suppression of the error value and the interference. The traditional distance-based membership function can not accurately reflect the relationship between the sample data and easily miss the abnormal sample, thus leading to the abnormal sample having the same membership value as the normal sample. In this paper, the membership function is modified with the idea of k-nearest neighbor. (4) The optimization of FLSSVR parameters and the multi-model connection method for fuzzy clustering algorithm are studied. Although the on-line clustering algorithm can effectively solve the problem of the small sample of the support vector machine, but at the same time the effective sub-model connection method is lacking, the invention provides a switch switching and weighting combination sub-model connection method applied to the performance evaluation strategy of the analog circuit, The validity of the proposed method is verified by the simulation experiment.
【学位授予单位】：渤海大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN710;TP18
，
本文编号：2510437
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