A novel stability computation approach for tactical missile autopilots is detailed. The limi- tations of traditional stability margins are exhibited. Then the vector margin is introduced and com- pared with sensitivit...A novel stability computation approach for tactical missile autopilots is detailed. The limi- tations of traditional stability margins are exhibited. Then the vector margin is introduced and com- pared with sensitivity function to show their essential relationship. The longitudinal three-loop auto- pilot for tactical missiles is presented and used as the baseline for all the available linear autopilots. Ten linear autopilot topologies using all the measurable feedback components are given with the iden- tical closed-loop characteristic equation and time-domain step response. However, the stability of the ten autopilots differs when considering the actuator dynamics, which limits their application. Then vector margin method is adopted to compute and evaluate the stability of all available autopi- lots. The analysis and computation results show that the vector margin method could better evaluate autopilot stability.展开更多
As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing disc...As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.展开更多
提出了一种模糊最优间隔分布矩阵分类器(Fuzzy Optimal-margin Distribution Matrix Classifier,FODMC)。该模型通过整合模糊隶属度理论与间隔分布优化机制,实现了矩阵结构信息的有效提取与异常值的鲁棒处理。具体而言,FODMC采用基于间...提出了一种模糊最优间隔分布矩阵分类器(Fuzzy Optimal-margin Distribution Matrix Classifier,FODMC)。该模型通过整合模糊隶属度理论与间隔分布优化机制,实现了矩阵结构信息的有效提取与异常值的鲁棒处理。具体而言,FODMC采用基于间隔分布的损失函数来优化分类边界,结合核范数正则化策略保持矩阵的低秩特性,并利用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)实现模型的高效训练。在多个基准数据集上的实验结果表明:与现有方法相比,FODMC在分类准确率、鲁棒性和泛化能力等方面均展现出显著优势,为矩阵数据分类问题提供了一种有效的解决方案。展开更多
对于二类目标特征选择问题,首先讨论了特征空间的线性可分性问题,并给出了其判别条件;其次,通过借鉴支撑矢量机原理,分析了特征可分性判据的基本性质;最后,依据各特征对分类间隔的贡献大小定义了特征有效率,并以此进行特征选择和特征空...对于二类目标特征选择问题,首先讨论了特征空间的线性可分性问题,并给出了其判别条件;其次,通过借鉴支撑矢量机原理,分析了特征可分性判据的基本性质;最后,依据各特征对分类间隔的贡献大小定义了特征有效率,并以此进行特征选择和特征空间降维.实测数据与网络公开UCI(University of california,Irvine)数据库的实验结果表明,与经典的Relief特征选择算法相比,该算法在识别性能和推广能力上明显有所提高.展开更多
基金Supported by the National Natural Science Foundation of China(61172182)
文摘A novel stability computation approach for tactical missile autopilots is detailed. The limi- tations of traditional stability margins are exhibited. Then the vector margin is introduced and com- pared with sensitivity function to show their essential relationship. The longitudinal three-loop auto- pilot for tactical missiles is presented and used as the baseline for all the available linear autopilots. Ten linear autopilot topologies using all the measurable feedback components are given with the iden- tical closed-loop characteristic equation and time-domain step response. However, the stability of the ten autopilots differs when considering the actuator dynamics, which limits their application. Then vector margin method is adopted to compute and evaluate the stability of all available autopi- lots. The analysis and computation results show that the vector margin method could better evaluate autopilot stability.
文摘As a set of supervised pattern recognition methods, support vector machines (SVMs) have been successfully applied to functional magnetic resonance imaging (fMRI) field, but few studies have focused on visualizing discriminative regions of whole brain between different cognitive tasks dynamically. This paper presents a SVM-based method for visualizing dynamically discriminative activation of whole-brain voxels between two kinds of tasks without any contrast. Our method provides a series of dynamic spatial discrimination maps (DSDMs), representing the temporal evolution of discriminative brain activation during a duty cycle and describing how the discriminating information changes over the duty cycle. The proposed method was applied to investigate discriminative brain functional activations of whole brain voxels dynamically based on a hand-motor task experiment. A set of DSDMs between left hand movement and right hand movement were reached. Our results demonstrated not only where but also when the discriminative activations of whole brain voxels occurred between left hand movement and right hand movement during one duty cycle.
文摘提出了一种模糊最优间隔分布矩阵分类器(Fuzzy Optimal-margin Distribution Matrix Classifier,FODMC)。该模型通过整合模糊隶属度理论与间隔分布优化机制,实现了矩阵结构信息的有效提取与异常值的鲁棒处理。具体而言,FODMC采用基于间隔分布的损失函数来优化分类边界,结合核范数正则化策略保持矩阵的低秩特性,并利用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)实现模型的高效训练。在多个基准数据集上的实验结果表明:与现有方法相比,FODMC在分类准确率、鲁棒性和泛化能力等方面均展现出显著优势,为矩阵数据分类问题提供了一种有效的解决方案。
文摘对于二类目标特征选择问题,首先讨论了特征空间的线性可分性问题,并给出了其判别条件;其次,通过借鉴支撑矢量机原理,分析了特征可分性判据的基本性质;最后,依据各特征对分类间隔的贡献大小定义了特征有效率,并以此进行特征选择和特征空间降维.实测数据与网络公开UCI(University of california,Irvine)数据库的实验结果表明,与经典的Relief特征选择算法相比,该算法在识别性能和推广能力上明显有所提高.
