针对原始的局部相位量化(Local Phase Quantization,LPQ)算法对具有模糊不变性的相位特征描述不准确、缺少对图像重要细节信息描述的缺点,提出了一种结合高斯拉普拉斯(Laplace of Gaussian,LoG)边缘检测和增强局部相位量化(Enhanced Loc...针对原始的局部相位量化(Local Phase Quantization,LPQ)算法对具有模糊不变性的相位特征描述不准确、缺少对图像重要细节信息描述的缺点,提出了一种结合高斯拉普拉斯(Laplace of Gaussian,LoG)边缘检测和增强局部相位量化(Enhanced Local Phase Quantization,ELPQ)的模糊图像识别算法,记为MrELPQ&MsLoG(Multi-resolution ELPQ and Multi-scale LoG)。首先,在频域中,将图像进行短时傅里叶变换后得到的实部与虚部进行正负量化和幅值量化,得到互补的符号特征ELPQ_S和幅值特征ELPQ_M;其次,在空间域中,利用多尺度高斯拉普拉斯与图像进行卷积得到图像空间域的边缘特征;最后,将频域上的符号特征ELPQ_S和幅值特征ELPQ_M与空间域上的边缘特征结合,生成最终的特征直方图,采用SVM进行识别。在有模糊干扰的Brodatz和KTH-TIPS纹理库中,文中提出的ELPQ算法相比原始的LPQ算法有较大的性能提升,且空间域和频域结合的MrELPQ&MsLoG算法能进一步提高算法的识别性能;在具有模糊的AR、Extend Yale B人脸库和实际拍摄的铁路扣件库中,将MrELPQ&MsLoG算法与目前模糊鲁棒性较好的算法进行对比发现,MrELPQ&MsLoG算法保持着较高的识别率。实验结果表明,MrELPQ&MsLoG算法对模糊具有较强的鲁棒性,且特征提取时间较短,具有实时性。展开更多
Takagi-Sugeno(T-S) fuzzy model is difficult to be linearized because of membership functions included.So,novel T-S fuzzy state transformation and T-S fuzzy feedback are proposed for the linearization of T-S fuzzy syst...Takagi-Sugeno(T-S) fuzzy model is difficult to be linearized because of membership functions included.So,novel T-S fuzzy state transformation and T-S fuzzy feedback are proposed for the linearization of T-S fuzzy system.The novel T-S fuzzy state transformation is the fuzzy combination of local linear transformation which transforms local linear models in the T-S fuzzy model into the local linear controllable canonical models.The fuzzy combination of local linear controllable canonical model gives controllable canonical T-S fuzzy model and then nonlinear feedback is obtained easily.After the linearization of T-S fuzzy model,a robust H∞ controller with the robustness of sliding model control(SMC) is designed.As a result,controlled T-S fuzzy system shows the performance of H∞ control and the robustness of SMC.展开更多
Aiming at a class of systems under parameter perturbations and unknown external disturbances, a method of fuzzy robust sliding mode control was proposed. Firstly, an integral sliding mode surface containing state feed...Aiming at a class of systems under parameter perturbations and unknown external disturbances, a method of fuzzy robust sliding mode control was proposed. Firstly, an integral sliding mode surface containing state feedback item was designed based on robust H∞ control theory. The robust state feedback control was utilized to substitute for the equivalent control of the traditional sliding mode control. Thus the robustness of systems sliding mode motion was improved even the initial states were unknown. Furthermore, when the upper bound of disturbance was unknown, the switching control logic was difficult to design, and the drawbacks of chattering in sliding mode control should also be considered simultaneously. To solve the above-mentioned problems, the fuzzy nonlinear method was applied to approximate the switching control term. Based on the Lyapunov stability theory, the parameter adaptive law which could guarantee the system stability was devised. The proposed control strategy could reduce the system chattering effectively. And the control input would not switch sharply, which improved the practicality of the sliding mode controller. Finally, simulation was conducted on system with parameter perturbations and unknown external disturbances. The result shows that the proposed method could enhance the approaching motion performance effectively. The chattering phenomenon is weakened, and the system possesses stronger robustness against parameter perturbations and external disturbances.展开更多
文摘针对原始的局部相位量化(Local Phase Quantization,LPQ)算法对具有模糊不变性的相位特征描述不准确、缺少对图像重要细节信息描述的缺点,提出了一种结合高斯拉普拉斯(Laplace of Gaussian,LoG)边缘检测和增强局部相位量化(Enhanced Local Phase Quantization,ELPQ)的模糊图像识别算法,记为MrELPQ&MsLoG(Multi-resolution ELPQ and Multi-scale LoG)。首先,在频域中,将图像进行短时傅里叶变换后得到的实部与虚部进行正负量化和幅值量化,得到互补的符号特征ELPQ_S和幅值特征ELPQ_M;其次,在空间域中,利用多尺度高斯拉普拉斯与图像进行卷积得到图像空间域的边缘特征;最后,将频域上的符号特征ELPQ_S和幅值特征ELPQ_M与空间域上的边缘特征结合,生成最终的特征直方图,采用SVM进行识别。在有模糊干扰的Brodatz和KTH-TIPS纹理库中,文中提出的ELPQ算法相比原始的LPQ算法有较大的性能提升,且空间域和频域结合的MrELPQ&MsLoG算法能进一步提高算法的识别性能;在具有模糊的AR、Extend Yale B人脸库和实际拍摄的铁路扣件库中,将MrELPQ&MsLoG算法与目前模糊鲁棒性较好的算法进行对比发现,MrELPQ&MsLoG算法保持着较高的识别率。实验结果表明,MrELPQ&MsLoG算法对模糊具有较强的鲁棒性,且特征提取时间较短,具有实时性。
基金Research financially supported by Changwon National University in 2009
文摘Takagi-Sugeno(T-S) fuzzy model is difficult to be linearized because of membership functions included.So,novel T-S fuzzy state transformation and T-S fuzzy feedback are proposed for the linearization of T-S fuzzy system.The novel T-S fuzzy state transformation is the fuzzy combination of local linear transformation which transforms local linear models in the T-S fuzzy model into the local linear controllable canonical models.The fuzzy combination of local linear controllable canonical model gives controllable canonical T-S fuzzy model and then nonlinear feedback is obtained easily.After the linearization of T-S fuzzy model,a robust H∞ controller with the robustness of sliding model control(SMC) is designed.As a result,controlled T-S fuzzy system shows the performance of H∞ control and the robustness of SMC.
基金Project(51476187)supported by the National Natural Science Foundation of China
文摘Aiming at a class of systems under parameter perturbations and unknown external disturbances, a method of fuzzy robust sliding mode control was proposed. Firstly, an integral sliding mode surface containing state feedback item was designed based on robust H∞ control theory. The robust state feedback control was utilized to substitute for the equivalent control of the traditional sliding mode control. Thus the robustness of systems sliding mode motion was improved even the initial states were unknown. Furthermore, when the upper bound of disturbance was unknown, the switching control logic was difficult to design, and the drawbacks of chattering in sliding mode control should also be considered simultaneously. To solve the above-mentioned problems, the fuzzy nonlinear method was applied to approximate the switching control term. Based on the Lyapunov stability theory, the parameter adaptive law which could guarantee the system stability was devised. The proposed control strategy could reduce the system chattering effectively. And the control input would not switch sharply, which improved the practicality of the sliding mode controller. Finally, simulation was conducted on system with parameter perturbations and unknown external disturbances. The result shows that the proposed method could enhance the approaching motion performance effectively. The chattering phenomenon is weakened, and the system possesses stronger robustness against parameter perturbations and external disturbances.
