To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introdu...To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.展开更多
To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering al...To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.展开更多
Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not...Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.展开更多
EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Lev...EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Levenberg-Marquar t b. This paper presents the relation between location errors and noise level on c ondition that the source number is known; if the source number is not known, the selected number in model may not equal to the actual one, and a computation is carried out and a corresponding discrimination criteria is proposed. Computer si mulation demonstrates that Levenberg-Marquardt algorithm is better than global methods if the source number is small.展开更多
Due to the encephalic tissues are highly irregular, three-dimensional （3D） modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction fr...Due to the encephalic tissues are highly irregular, three-dimensional （3D） modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction from magnetic reso- nance （MR） images of head, which is helpful to surgery planning and tumor localization. A heuristic algorithm is pro- posed foi＂ surface triangle mesh generation with preserved features, and the diagonal length is regarded as the heuristic information to optimize the shape of triangle. The experimental results show that our approach not only reduces the computational complexity, but also completes 3D visualization with good quality.展开更多
基金Project(61403422)supported by the National Natural Science Foundation of ChinaProject(17C1084)supported by Hunan Education Department Science Foundation of ChinaProject(17ZD02)supported by Hunan University of Arts and Science,China
文摘To overcome the deficiencies of high computational complexity and low convergence speed in traditional neural networks, a novel bio-inspired machine learning algorithm named brain emotional learning （BEL） is introduced. BEL mimics the emotional learning mechanism in brain which has the superior features of fast learning and quick reacting. To further improve the performance of BEL in data analysis, genetic algorithm （GA） is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in BEL neural network. The integrated algorithm named GA-BEL combines the advantages of the fast learning of BEL, and the global optimum solution of GA. GA-BEL has been tested on a real-world chaotic time series of geomagnetic activity index for prediction, eight benchmark datasets of university California at Irvine （UCI） and a functional magnetic resonance imaging （fMRI） dataset for classifications. The comparisons of experimental results have shown that the proposed GA-BEL algorithm is more accurate than the original BEL in prediction, and more effective when dealing with large-scale classification problems. Further, it outperforms most other traditional algorithms in terms of accuracy and execution speed in both prediction and classification applications.
基金Supported by National Natural Science Foundation of China (No. 60872065)
文摘To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.
文摘Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.
文摘EEG inverse problem has great significance and importance for both cli nical and research applications. It discusses EEG dipole source localization pro blems solved by nonlinear local optimization methods, such as Levenberg-Marquar t b. This paper presents the relation between location errors and noise level on c ondition that the source number is known; if the source number is not known, the selected number in model may not equal to the actual one, and a computation is carried out and a corresponding discrimination criteria is proposed. Computer si mulation demonstrates that Levenberg-Marquardt algorithm is better than global methods if the source number is small.
基金supported by the National Natural Science Foundation of China(No.61202169)
文摘Due to the encephalic tissues are highly irregular, three-dimensional （3D） modeling of brain always leads to compli- cated computing. In this paper, we explore an efficient method for brain surface reconstruction from magnetic reso- nance （MR） images of head, which is helpful to surgery planning and tumor localization. A heuristic algorithm is pro- posed foi＂ surface triangle mesh generation with preserved features, and the diagonal length is regarded as the heuristic information to optimize the shape of triangle. The experimental results show that our approach not only reduces the computational complexity, but also completes 3D visualization with good quality.
