工业控制场合中,需要获取非线性被控对象的结构特性,而系统动态响应的数据直接从外部特征上反映了非线性系统结构关系.为了充分利用非线性动态系统响应过程中的数据,本文提出了一种基于滑动数据窗口(sliding data window)的贝叶斯-高斯...工业控制场合中,需要获取非线性被控对象的结构特性,而系统动态响应的数据直接从外部特征上反映了非线性系统结构关系.为了充分利用非线性动态系统响应过程中的数据,本文提出了一种基于滑动数据窗口(sliding data window)的贝叶斯-高斯神经网络(SW-BGNN)模型.该模型将数据融合于网络模型结构中,借助于贝叶斯推理和高斯假设,利用滑动窗口数据,实现非线性动态系统的辨识和预测.整个SW-BGNN本身需要确定的参数很少,因此运算的时间很短,适合于非线性动态系统的在线辨识.将SW-BGNN应用于几个非线性动态系统的辨识和预测,仿真试验结果表明了SW--BGNN模型的有效性.展开更多
The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for...The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /展开更多
We study the effect of time-periodic coupling strength on the spiking coherence of Newman-Watts networks of Hodgkin-Huxley(HH) neurons with non-Gaussian noise.It is found that the spiking can exhibit coherence resonan...We study the effect of time-periodic coupling strength on the spiking coherence of Newman-Watts networks of Hodgkin-Huxley(HH) neurons with non-Gaussian noise.It is found that the spiking can exhibit coherence resonance(CR) when the extent of deviation of non-Gaussian noise from Gaussian noise and the amplitude of the coupling strength are varied.In particular,coherence bi-resonance(CBR) is observed when the frequency of the coupling strength is varied,and the CBR is always observed when the frequency is equal to,or a multiple of,the spiking period,manifesting as the locking between the frequencies of the spiking and the coupling strength.The results show that a time-periodic coupling strength may play a more constructive and efficient role in enhancing the spiking coherence of the neuronal networks than a constant coupling strength.These findings provide insight into the role of time-periodic coupling strength for enhancing the time precision of information processing in neuronal networks.展开更多
The spiking behavior with varying time delay in scale-free networks of Hodgkin-Huxley neurons with non-Gaussian noise has been studied,and the effect of non-Gaussian noise on the delay-induced spiking behavior is disc...The spiking behavior with varying time delay in scale-free networks of Hodgkin-Huxley neurons with non-Gaussian noise has been studied,and the effect of non-Gaussian noise on the delay-induced spiking behavior is discussed. It was found that multiple spatio-temporal resonances occur when the delay lengths are integer multiples of the spiking periods,and the resonances may be strengthened when the non-Gaussian noise is appropriate. This result shows that time delays can optimize the spiking temporal regularity and spatial synchronization,and appropriate non-Gaussian noise may enhance the delay-induced spiking behaviors. Our findings may help to further understand the joint roles of non-Gaussian noise and time delays in the spiking activity of scale-free neuronal networks.展开更多
Machine learning techniques which are about the construction and study of system that can learn from data are combined with many application fields.A method on ionospheric total electron content(TEC)mapping is propose...Machine learning techniques which are about the construction and study of system that can learn from data are combined with many application fields.A method on ionospheric total electron content(TEC)mapping is proposed based on radical basis function(RBF)neural network improved by Gaussian mixture model(GMM).Due to the complicated ionospheric behavior over China,GMM is used to determine the center of basis function in the unsupervised training process.Gradient descent is performed to update the weights function on a sum of squared output error function in the supervised learning process.The TEC values from the center for orbit determination in Europe(CODE)global ionospheric maps covering the period from 2007to 2010 are used to investigate the performance of the developed network model.For independent validation,the simulated TEC values at different latitudes(20°N,30°N and 40°N)along 120°E longitude are analyzed and evaluated.The results show that the simulated TEC from the RBF network based model has good agreement with the observed CODE TEC with acceptable errors.The theoretical research indicates that RBF can offer a powerful and reliable alternative to the design of ionospheric TEC forecast technologies and thus make a significant contribution to the ionospheric modeling efforts in China.展开更多
文摘工业控制场合中,需要获取非线性被控对象的结构特性,而系统动态响应的数据直接从外部特征上反映了非线性系统结构关系.为了充分利用非线性动态系统响应过程中的数据,本文提出了一种基于滑动数据窗口(sliding data window)的贝叶斯-高斯神经网络(SW-BGNN)模型.该模型将数据融合于网络模型结构中,借助于贝叶斯推理和高斯假设,利用滑动窗口数据,实现非线性动态系统的辨识和预测.整个SW-BGNN本身需要确定的参数很少,因此运算的时间很短,适合于非线性动态系统的在线辨识.将SW-BGNN应用于几个非线性动态系统的辨识和预测,仿真试验结果表明了SW--BGNN模型的有效性.
文摘The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /
基金supported by the Natural Science Foundation of Shandong Province of China (ZR2009AM016)
文摘We study the effect of time-periodic coupling strength on the spiking coherence of Newman-Watts networks of Hodgkin-Huxley(HH) neurons with non-Gaussian noise.It is found that the spiking can exhibit coherence resonance(CR) when the extent of deviation of non-Gaussian noise from Gaussian noise and the amplitude of the coupling strength are varied.In particular,coherence bi-resonance(CBR) is observed when the frequency of the coupling strength is varied,and the CBR is always observed when the frequency is equal to,or a multiple of,the spiking period,manifesting as the locking between the frequencies of the spiking and the coupling strength.The results show that a time-periodic coupling strength may play a more constructive and efficient role in enhancing the spiking coherence of the neuronal networks than a constant coupling strength.These findings provide insight into the role of time-periodic coupling strength for enhancing the time precision of information processing in neuronal networks.
基金supported by the Natural Science Foundation of Shandong Province (ZR2009AM016)
文摘The spiking behavior with varying time delay in scale-free networks of Hodgkin-Huxley neurons with non-Gaussian noise has been studied,and the effect of non-Gaussian noise on the delay-induced spiking behavior is discussed. It was found that multiple spatio-temporal resonances occur when the delay lengths are integer multiples of the spiking periods,and the resonances may be strengthened when the non-Gaussian noise is appropriate. This result shows that time delays can optimize the spiking temporal regularity and spatial synchronization,and appropriate non-Gaussian noise may enhance the delay-induced spiking behaviors. Our findings may help to further understand the joint roles of non-Gaussian noise and time delays in the spiking activity of scale-free neuronal networks.
基金supported by the National Natural Science Foundation of China(Grant No.41104096)
文摘Machine learning techniques which are about the construction and study of system that can learn from data are combined with many application fields.A method on ionospheric total electron content(TEC)mapping is proposed based on radical basis function(RBF)neural network improved by Gaussian mixture model(GMM).Due to the complicated ionospheric behavior over China,GMM is used to determine the center of basis function in the unsupervised training process.Gradient descent is performed to update the weights function on a sum of squared output error function in the supervised learning process.The TEC values from the center for orbit determination in Europe(CODE)global ionospheric maps covering the period from 2007to 2010 are used to investigate the performance of the developed network model.For independent validation,the simulated TEC values at different latitudes(20°N,30°N and 40°N)along 120°E longitude are analyzed and evaluated.The results show that the simulated TEC from the RBF network based model has good agreement with the observed CODE TEC with acceptable errors.The theoretical research indicates that RBF can offer a powerful and reliable alternative to the design of ionospheric TEC forecast technologies and thus make a significant contribution to the ionospheric modeling efforts in China.
