Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed ...Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.展开更多
Spontaneous combustion of coal is a major cause of coal mine fires.It not only poses a severe hazard to the safe extraction of coal resources,but also jeopardizes the safety of mine workers.The development of a scient...Spontaneous combustion of coal is a major cause of coal mine fires.It not only poses a severe hazard to the safe extraction of coal resources,but also jeopardizes the safety of mine workers.The development of a scientific management system of coal spontaneous combustion is of vital importance to the safe production of coal mine.This paper provides a comparative analysis of a range of worldwide prediction techniques and methods for coal spontaneous combustion,and systematically introduces the trigger action response plans(TARPs)system used in Australian coal mines for managing the spontaneous heating of coal.An artificial neural network model has been established on the basis of real coal mine operational conditions.Through studying and training the neural network model,prediction errors can be controlled within the allowable range.The trained model is then applied to the conditions of Nos.1 and 3 coal seams located in Weijiadi Coal Mine to demonstrate its feasibility for spontaneous combustion assessment.Based upon the TARPs system which is commonly used in Australian longwall mines,a TARPs system has been developed for Weijiadi Coal Mine to assist the management of spontaneous combustion hazard and ensure the safe operation of its mining activities.展开更多
By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power pla...By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.展开更多
This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and us...This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.展开更多
The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic tim...The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.展开更多
A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance...A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.展开更多
The control system determines the effectiveness of an underwater hydraulic shock shovel. This paper begins by analyzing the working principles of these shovels and explains the importance of their control systems. A n...The control system determines the effectiveness of an underwater hydraulic shock shovel. This paper begins by analyzing the working principles of these shovels and explains the importance of their control systems. A new type of control system’s mathematical model was built and analyzed according to those principles. Since the initial control system’s response time could not fulfill the design requirements, a PID controller was added to the control system. System response time was still slower than required, so a neural network was added to nonlinearly regulate the proportional element, integral element and derivative element coefficients of the PID controller. After these improvements to the control system, system parameters fulfilled the design requirements. The working performance of electrically-controlled parts such as the rapidly moving high speed switch valve is largely determined by the control system. Normal control methods generally can’t satisfy a shovel’s requirements, so advanced and normal control methods were combined to improve the control system, bringing good results.展开更多
Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Proj...Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.展开更多
To reduce network redundancy,innetwork caching is considered in many future Internet architectures,such as Information Centric Networking.In in-network caching system,the item sojourn time of LRU(Least Recently Used) ...To reduce network redundancy,innetwork caching is considered in many future Internet architectures,such as Information Centric Networking.In in-network caching system,the item sojourn time of LRU(Least Recently Used) replacement policy is an important issue for two reasons:firstly,LRU is one of the most common used cache policy;secondly,item sojourn time is positively correlated to the hit probability,so this metric parameter could be useful to design the caching system.However,to the best of our knowledge,the sojourn time hasn't been studied theoretically so far.In this paper,we first model the LRU cache policy by Markov chain.Then an approximate closedform expression of the item expectation sojourn time is provided through the theory of stochastic service system,which is a function of the item request rates and cache size.Finally,extensive simulation results are illustrated to show that the expression is a good approximation of the item sojourn time.展开更多
An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The st...An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.展开更多
Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characte...Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characterize and optimize a particular mechanical minipump. The optimization work was conducted to cope with the conflict between pressure head and hydraulic efficiency by an improved back-propagation neural network （BPNN） with the non-dominated sorting genetic algorithm-II （NSGA-II）. The improved BPNN was utilized to predicate hydraulic performance and, moreover, was modified to improve the prediction accuracy. The NSGA-II was processed for minipump multi-objective optimization which is dominated by four impeller dimensions. During hydraulic optimization, the processing feasibility was also taken into consideration. Experiments were conducted to validate the above optimization methods. It was proved that the optimized minipump was improved by about 24 % in pressure head and 4.75 % in hydraulic efficiency compared to the original designed prototype. Meanwhile, the sensitivity test was used to analyze the influence of the four impeller dimensions. It was found that the blade outlet angle β2 and the impeller inlet diameter Do significantly influence the pressure head H and the hydraulic efficiency η, respec- tively. Detailed internal flow fields showed that the optimum model can relieve the impeller wake and improve both the pressure distribution and flow orientation.展开更多
文摘Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.
基金provided for this work by the China Scholarship CouncilNational Natural Science Funds of China(No.51304212)
文摘Spontaneous combustion of coal is a major cause of coal mine fires.It not only poses a severe hazard to the safe extraction of coal resources,but also jeopardizes the safety of mine workers.The development of a scientific management system of coal spontaneous combustion is of vital importance to the safe production of coal mine.This paper provides a comparative analysis of a range of worldwide prediction techniques and methods for coal spontaneous combustion,and systematically introduces the trigger action response plans(TARPs)system used in Australian coal mines for managing the spontaneous heating of coal.An artificial neural network model has been established on the basis of real coal mine operational conditions.Through studying and training the neural network model,prediction errors can be controlled within the allowable range.The trained model is then applied to the conditions of Nos.1 and 3 coal seams located in Weijiadi Coal Mine to demonstrate its feasibility for spontaneous combustion assessment.Based upon the TARPs system which is commonly used in Australian longwall mines,a TARPs system has been developed for Weijiadi Coal Mine to assist the management of spontaneous combustion hazard and ensure the safe operation of its mining activities.
基金supported by the project of "SDUST Qunxing Program"(No.qx0902075)
文摘By combining the Back-Propagation （BP） neural network with conventional proportional Integral Derivative （PID） controller, a new temperature control strategy of the export steam in supercritical electric power plant is put forward. This scheme can effectively overcome the large time delay, inertia of the export steam and the influencee of object in varying operational parameters. Thus excellent control quality is obtaitud. The present paper describes the development and application of neural network based controller to control the temperature of the boiler＇s export steam. Through simulation in various situations, it validates that the control quality of this control system is apparently superior to the conventional PID control system.
文摘This paper presents a method of modeling a fuzzy system with fuzzy and nonlinear border,obtaining systematic structure by clustering analysis,applying BP network (BPN) to generate rule bases antecedent function and using RBF network (RBFN) to approximate each rules conclusion function not only because of efficient capability of approximation nonlinear function of BPN and RBFN but also because of quickness of training speed of RBFN. In addition,structure design and training of relevant networks are discussed in detail. Finally,the structure optimization and overstudy of RBFN are discussed.
文摘The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.
文摘A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.
基金the 863 Program Item of Hi-tech Research and Development Program of China Foundation under Grant No.2002AA602012-1Harbin Engineering University Foundation under Grant No. HEUFT05071the Research Fund for the Doctoral Program of Higher Education under Grant No.20070217016.
文摘The control system determines the effectiveness of an underwater hydraulic shock shovel. This paper begins by analyzing the working principles of these shovels and explains the importance of their control systems. A new type of control system’s mathematical model was built and analyzed according to those principles. Since the initial control system’s response time could not fulfill the design requirements, a PID controller was added to the control system. System response time was still slower than required, so a neural network was added to nonlinearly regulate the proportional element, integral element and derivative element coefficients of the PID controller. After these improvements to the control system, system parameters fulfilled the design requirements. The working performance of electrically-controlled parts such as the rapidly moving high speed switch valve is largely determined by the control system. Normal control methods generally can’t satisfy a shovel’s requirements, so advanced and normal control methods were combined to improve the control system, bringing good results.
基金Under the auspices of National Natural Science Foundation of China (No 40871251)Knowledge Innovation Programs of Chinese Academy of Sciences (No KZCX2-YW-141)
文摘Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.
文摘To reduce network redundancy,innetwork caching is considered in many future Internet architectures,such as Information Centric Networking.In in-network caching system,the item sojourn time of LRU(Least Recently Used) replacement policy is an important issue for two reasons:firstly,LRU is one of the most common used cache policy;secondly,item sojourn time is positively correlated to the hit probability,so this metric parameter could be useful to design the caching system.However,to the best of our knowledge,the sojourn time hasn't been studied theoretically so far.In this paper,we first model the LRU cache policy by Markov chain.Then an approximate closedform expression of the item expectation sojourn time is provided through the theory of stochastic service system,which is a function of the item request rates and cache size.Finally,extensive simulation results are illustrated to show that the expression is a good approximation of the item sojourn time.
文摘An energy-saving scheme for pumping units via intermission start-stop performance is proposed. Because of the complexity of the oil extraction process, Fuzzy Neural Network (FNN) intelligent control is adopted. The structure of the Takagi-Sugeno (T-S) fuzzy neural network model is introduced and modified. FNNs are trained with sample information from oil fields and expert knowledge. Finally, pumping unit energy-saving FNN software, which cuts down power costs substantially, is presented.
文摘Minipump is widely used in microfluidics system, active cooling system, etc. But building a high efficiency minipump is still a challenging problem. In this paper, a systematic method was developed to design, characterize and optimize a particular mechanical minipump. The optimization work was conducted to cope with the conflict between pressure head and hydraulic efficiency by an improved back-propagation neural network （BPNN） with the non-dominated sorting genetic algorithm-II （NSGA-II）. The improved BPNN was utilized to predicate hydraulic performance and, moreover, was modified to improve the prediction accuracy. The NSGA-II was processed for minipump multi-objective optimization which is dominated by four impeller dimensions. During hydraulic optimization, the processing feasibility was also taken into consideration. Experiments were conducted to validate the above optimization methods. It was proved that the optimized minipump was improved by about 24 % in pressure head and 4.75 % in hydraulic efficiency compared to the original designed prototype. Meanwhile, the sensitivity test was used to analyze the influence of the four impeller dimensions. It was found that the blade outlet angle β2 and the impeller inlet diameter Do significantly influence the pressure head H and the hydraulic efficiency η, respec- tively. Detailed internal flow fields showed that the optimum model can relieve the impeller wake and improve both the pressure distribution and flow orientation.
