On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Mal...On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.展开更多
The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e ar...The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.展开更多
Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was...Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.展开更多
Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main...Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, includ...Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, including mathematical model combined with neural network(NN), rule model based on empirical knowledge and model-choosing coordinator. Via metallurgic mechanism analysis and material balance computation, a mathematical model calculated the sulfur content in agglomerate by the material balance equation with some parameters predicted by NN method. In the other model, the relationship between sulfur content and key factors was described in the form of expert rules. The model-choosing coordinator based on fuzzy logic was introduced to decide the weight of result of each model according to process conditions. The model was tested by industrial application data and produced a relatively satisfactory prediction error. The model also preferably reflected the varying tendency of sulfur content in agglomerate as the evidence of its prediction performance.展开更多
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
基金Supported by Brilliant Youth Fund in Hebei Province
文摘On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
文摘The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both tempor al and spatial variabilities. In this article, a rainfall-runoff model using th e artificial neural networks (ANN) is proposed for simula ting the runoff in storm events. The study uses the data from a coa stal forest catchment located in Seto Inland Sea, Japan. This article studies the accuracy of the short-term rainfall forecast obta ined by ANN time-series analysis techniques and using antecedent rainfa ll depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rai nfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy.
文摘Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.
文摘Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
文摘Based on the idea of fusing modeling, an integrated prediction model for sintering process was proposed. A framework for sulfur content prediction was established, which integrated multi modeling ways together, including mathematical model combined with neural network(NN), rule model based on empirical knowledge and model-choosing coordinator. Via metallurgic mechanism analysis and material balance computation, a mathematical model calculated the sulfur content in agglomerate by the material balance equation with some parameters predicted by NN method. In the other model, the relationship between sulfur content and key factors was described in the form of expert rules. The model-choosing coordinator based on fuzzy logic was introduced to decide the weight of result of each model according to process conditions. The model was tested by industrial application data and produced a relatively satisfactory prediction error. The model also preferably reflected the varying tendency of sulfur content in agglomerate as the evidence of its prediction performance.
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
