To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real tim...To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.展开更多
运动想象是基于脑电图信号构造脑机接口的重要手段之一,当前主流方法依赖于单任务的特征提取方法或卷积神经网络模型,无法同时兼顾时空、频段特征的复杂变化。为此,提出一种基于多任务卷积神经网络的运动想象脑电解码方法。该模型包含...运动想象是基于脑电图信号构造脑机接口的重要手段之一,当前主流方法依赖于单任务的特征提取方法或卷积神经网络模型,无法同时兼顾时空、频段特征的复杂变化。为此,提出一种基于多任务卷积神经网络的运动想象脑电解码方法。该模型包含时空特征提取任务和频段提取任务;采用卷积操作分别提取时域、空域特征,以及小波卷积提取深度频段特征;最终构建多任务目标函数优化卷积神经网络模型,实现多种特征类型的互补。在BCI Competition IV 2a和2b公开数据集上的实验结果表明,与现有单任务方法或模型相比,所提出的新模型提高了脑电特征学习能力,在两个数据集上分别获得了84.7%和80.6%的平均分类准确率,提升了运动想象解码性能。展开更多
Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications i...Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human,driver safety during driving,pain monitoring during surgery etc.A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region,where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion.To reduce the number of generated sub-graphs,overlap ratio metric is utilized for this purpose.After encoding the final selected sub-graphs,binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification.Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level.Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion(SAVEE)database and the final system accuracy was 90.00%.The results show significant accuracy improvements(about 2%)by the proposed system in comparison to current published works in SAVEE database.展开更多
基金Project(50276005) supported by the National Natural Science Foundation of China Projects (2006CB705400, 2003CB716206) supported by National Basic Research Program of China
文摘To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.
文摘运动想象是基于脑电图信号构造脑机接口的重要手段之一,当前主流方法依赖于单任务的特征提取方法或卷积神经网络模型,无法同时兼顾时空、频段特征的复杂变化。为此,提出一种基于多任务卷积神经网络的运动想象脑电解码方法。该模型包含时空特征提取任务和频段提取任务;采用卷积操作分别提取时域、空域特征,以及小波卷积提取深度频段特征;最终构建多任务目标函数优化卷积神经网络模型,实现多种特征类型的互补。在BCI Competition IV 2a和2b公开数据集上的实验结果表明,与现有单任务方法或模型相比,所提出的新模型提高了脑电特征学习能力,在两个数据集上分别获得了84.7%和80.6%的平均分类准确率,提升了运动想象解码性能。
文摘Recent years have seen an explosion in graph data from a variety of scientific,social and technological fields.From these fields,emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human,driver safety during driving,pain monitoring during surgery etc.A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region,where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion.To reduce the number of generated sub-graphs,overlap ratio metric is utilized for this purpose.After encoding the final selected sub-graphs,binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification.Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level.Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion(SAVEE)database and the final system accuracy was 90.00%.The results show significant accuracy improvements(about 2%)by the proposed system in comparison to current published works in SAVEE database.
