To improve magnetotelluric(MT)nonlinear inversion accuracy and stability,this work introduces the deep belief network(DBN)algorithm.Firstly,a network frame is set up for training in different 2D MT models.The network ...To improve magnetotelluric(MT)nonlinear inversion accuracy and stability,this work introduces the deep belief network(DBN)algorithm.Firstly,a network frame is set up for training in different 2D MT models.The network inputs are the apparent resistivities of known models,and the outputs are the model parameters.The optimal network structure is achieved by determining the numbers of hidden layers and network nodes.Secondly,the learning process of the DBN is implemented to obtain the optimal solution of network connection weights for known geoelectric models.Finally,the trained DBN is verified through inversion tests,in which the network inputs are the apparent resistivities of unknown models,and the outputs are the corresponding model parameters.The experiment results show that the DBN can make full use of the global searching capability of the restricted Boltzmann machine(RBM)unsupervised learning and the local optimization of the back propagation(BP)neural network supervised learning.Comparing to the traditional neural network inversion,the calculation accuracy and stability of the DBN for MT data inversion are improved significantly.And the tests on synthetic data reveal that this method can be applied to MT data inversion and achieve good results compared with the least-square regularization inversion.展开更多
A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spect...A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.展开更多
An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-v...An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.展开更多
针对复杂化工过程非平稳性、事件驱动性导致的关键指标参数难以精确软测量的问题,提出了一种事件驱动的深度信念网络(event-driven deep belief network,EDDBN)软测量模型设计方法。首先,获取化工过程运行数据并搭建深度信念网络(driven...针对复杂化工过程非平稳性、事件驱动性导致的关键指标参数难以精确软测量的问题,提出了一种事件驱动的深度信念网络(event-driven deep belief network,EDDBN)软测量模型设计方法。首先,获取化工过程运行数据并搭建深度信念网络(driven deep belief network,DBN)模型,以数据驱动的方式对DBN模型进行训练,获得基于DBN的软测量模型。其次,根据DBN模型的训练误差变化特性定义事件,当积极事件发生时会加速当前模型参数的学习步长,当消极事件发生时会跳过当前数据样本并直接进入下一时刻的数据样本学习。这种事件驱动的选择性学习策略不仅能够有效地优化软测量模型训练过程,而且还能降低计算复杂度。同时,通过构造基于马尔可夫链的动态学习过程,分析任意连续两次事件对应输出性能势之差的有界性,给出了EDDBN训练过程的收敛性分析。最后,将EDDBN软测量模型用于湿法烟气脱硫系统二氧化硫(SO_(2))浓度软测量实验,结果表明所提出的EDDBN软测量模型能够在非平稳运行工况下实现对SO_(2)浓度快速、精确地预测分析,并且计算复杂度在数据集(1)和数据集(2)上分别降低约63.83%和63.33%。展开更多
基金Project(41304090)supported by the National Natural Science Foundation of ChinaProject(2016YFC0303104)supported by the National Key Research and Development Project of ChinaProject(DY135-S1-1-07)supported by Ocean 13th Five-Year International Marine Resources Survey and Development of China
文摘To improve magnetotelluric(MT)nonlinear inversion accuracy and stability,this work introduces the deep belief network(DBN)algorithm.Firstly,a network frame is set up for training in different 2D MT models.The network inputs are the apparent resistivities of known models,and the outputs are the model parameters.The optimal network structure is achieved by determining the numbers of hidden layers and network nodes.Secondly,the learning process of the DBN is implemented to obtain the optimal solution of network connection weights for known geoelectric models.Finally,the trained DBN is verified through inversion tests,in which the network inputs are the apparent resistivities of unknown models,and the outputs are the corresponding model parameters.The experiment results show that the DBN can make full use of the global searching capability of the restricted Boltzmann machine(RBM)unsupervised learning and the local optimization of the back propagation(BP)neural network supervised learning.Comparing to the traditional neural network inversion,the calculation accuracy and stability of the DBN for MT data inversion are improved significantly.And the tests on synthetic data reveal that this method can be applied to MT data inversion and achieve good results compared with the least-square regularization inversion.
基金supported by the KERI Primary Research Program through the Korea Research Council for Industrial Science & Technology funded by the Ministry of Science,ICT and Future Planning (No.15-12-N0101-46)
文摘A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.
基金supported by Inha University Research Grant,Korea
文摘An enhanced algorithm is proposed to recognize multi-channel electromyography(EMG) patterns using deep belief networks(DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics.Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55%(p=9.82×10-12) higher than linear discriminant analysis(LDA) and 2.89%(p=1.94×10-5) higher than support vector machine(SVM). Further, the DBN is better than shallow learning algorithms or back propagation(BP), and this model is effective for an EMG-based user-interfaced system.
文摘针对复杂化工过程非平稳性、事件驱动性导致的关键指标参数难以精确软测量的问题,提出了一种事件驱动的深度信念网络(event-driven deep belief network,EDDBN)软测量模型设计方法。首先,获取化工过程运行数据并搭建深度信念网络(driven deep belief network,DBN)模型,以数据驱动的方式对DBN模型进行训练,获得基于DBN的软测量模型。其次,根据DBN模型的训练误差变化特性定义事件,当积极事件发生时会加速当前模型参数的学习步长,当消极事件发生时会跳过当前数据样本并直接进入下一时刻的数据样本学习。这种事件驱动的选择性学习策略不仅能够有效地优化软测量模型训练过程,而且还能降低计算复杂度。同时,通过构造基于马尔可夫链的动态学习过程,分析任意连续两次事件对应输出性能势之差的有界性,给出了EDDBN训练过程的收敛性分析。最后,将EDDBN软测量模型用于湿法烟气脱硫系统二氧化硫(SO_(2))浓度软测量实验,结果表明所提出的EDDBN软测量模型能够在非平稳运行工况下实现对SO_(2)浓度快速、精确地预测分析,并且计算复杂度在数据集(1)和数据集(2)上分别降低约63.83%和63.33%。
