In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and...In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.展开更多
The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis s...The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.展开更多
Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation proced...Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.展开更多
Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The m...Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.展开更多
A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently,...A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.展开更多
Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic...Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.展开更多
本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型...本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.展开更多
The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power.Wind power is the clean,free and conservative renewable energy.It is necessary to predict the wind speed,...The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power.Wind power is the clean,free and conservative renewable energy.It is necessary to predict the wind speed,to implement wind power generation.This paper proposes a new model,named WT-GWO-BPNN,by integrating Wavelet Transform(WT),Back Propagation Neural Network(BPNN)and GreyWolf Optimization(GWO).The wavelet transform is adopted to decompose the original time series data(wind speed)into approximation and detailed band.GWO-BPNN is applied to predict the wind speed.GWO is used to optimize the parameters of back propagation neural network and to improve the convergence state.This work uses wind power data of six months with 25,086 data points to test and verify the performance of the proposed model.The proposed work,WT-GWO-BPNN,predicts the wind speed using a three-step procedure and provides better results.Mean Absolute Error(MAE),Mean Squared Error(MSE),Mean absolute percentage error(MAPE)and Root mean squared error(RMSE)are calculated to validate the performance of the proposed model.Experimental results demonstrate that the proposed model has better performance when compared to other methods in the literature.展开更多
A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and i...A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.展开更多
An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circ...An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.展开更多
We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic bi...We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.展开更多
Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work...Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.展开更多
文摘In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.
文摘The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.
文摘Time series analysis plays an important role in hydrologic forecasting,while the key to this analysis is to establish a proper model.This paper presents a time series neural network model with back propagation procedure for hydrologic forecasting.Free from the disadvantages of previous models,the model can be parallel to operate information flexibly and rapidly.It excels in the ability of nonlinear mapping and can learn and adjust by itself,which gives the model a possibility to describe the complex nonlinear hydrologic process.By using directly a training process based on a set of previous data, the model can forecast the time series of stream flow.Moreover,two practical examples were used to test the performance of the time series neural network model.Results confirm that the model is efficient and feasible.
文摘Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.
文摘A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.
文摘Molding and simulation of time series prediction based on dynamic neural network(NN) are studied. Prediction model for non-linear and time-varying system is proposed based on dynamic Jordan NN. Aiming at the intrinsic defects of back-propagation (BP) algorithm that cannot update network weights incrementally, a hybrid algorithm combining the temporal difference (TD) method with BP algorithm to train Jordan NN is put forward. The proposed method is applied to predict the ash content of clean coal in jigging production real-time and multi-step. A practical example is also given and its application results indicate that the method has better performance than others and also offers a beneficial reference to the prediction of nonlinear time series.
文摘本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.
文摘The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power.Wind power is the clean,free and conservative renewable energy.It is necessary to predict the wind speed,to implement wind power generation.This paper proposes a new model,named WT-GWO-BPNN,by integrating Wavelet Transform(WT),Back Propagation Neural Network(BPNN)and GreyWolf Optimization(GWO).The wavelet transform is adopted to decompose the original time series data(wind speed)into approximation and detailed band.GWO-BPNN is applied to predict the wind speed.GWO is used to optimize the parameters of back propagation neural network and to improve the convergence state.This work uses wind power data of six months with 25,086 data points to test and verify the performance of the proposed model.The proposed work,WT-GWO-BPNN,predicts the wind speed using a three-step procedure and provides better results.Mean Absolute Error(MAE),Mean Squared Error(MSE),Mean absolute percentage error(MAPE)and Root mean squared error(RMSE)are calculated to validate the performance of the proposed model.Experimental results demonstrate that the proposed model has better performance when compared to other methods in the literature.
文摘A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.
基金Supported by National Natural Science Foundation (60274015) the "863" Program of P, R. China (2002AA412420)
文摘An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.
文摘We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.
文摘Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.
