A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and com- pensate for the fiber optic gyro (FOG) bias drift caused by temperature. In the network, FOG tempe...A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and com- pensate for the fiber optic gyro (FOG) bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter (UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise (-20-20℃) and drop (70-20℃) tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation (BP) and UKF network models.展开更多
A new thermal ring-opening polymerization technique for 1, 1, 3, 3-tetra-ph enyl-1, 3-disilacyclobutane (TPDC) based on the use of metal nanoparticles produced by pulsed laser ablation was investigated. This method ...A new thermal ring-opening polymerization technique for 1, 1, 3, 3-tetra-ph enyl-1, 3-disilacyclobutane (TPDC) based on the use of metal nanoparticles produced by pulsed laser ablation was investigated. This method facilitates the synthesis of polydiphenysilylenemethyle (PDPhSM) thin film, which is difficult to make by conventional methods because of its insolubility and high melting point. TPDC was first evaporated on silicon substrates and then exposed to metal nanoparticles deposition by pulsed laser ablation prior to heat treatment.The TPDC films with metal nanoparticles were heated in an electric furnace in air atmosphere to induce ring-opening polymerization of TPDC. The film thicknesses before and after polymerization were measured by a stylus profilometer. Since the polymerization process competes with re-evaporation of TPDC during the heating, the thickness ratio of the polymer to the monomer was defined as the polymerization efficiency, which depends greatly on the technology conditions. Therefore, a well trained radial base function neural network model was constructed to approach the complex nonlinear relationship. Moreover, a particle swarm algorithm was firstly introduced to search for an optimum technology directly from RBF neural network model. This ensures that the fabrication of thin film with appropriate properties using pulsed laser ablation requires no in-depth understanding of the entire behavior of the technology conditions.展开更多
A developmental research has been carried out to deal with the high performance of Cu-Cr-Zr-Mg lead frame alloy by artificial neural network (ANN). Using the cold working to assist in the aging hardening can improve t...A developmental research has been carried out to deal with the high performance of Cu-Cr-Zr-Mg lead frame alloy by artificial neural network (ANN). Using the cold working to assist in the aging hardening can improve the the hardness and electrical conductivity properties of Cu-Cr-Zr-Mg lead frame alloy. This paper studies the effect of different extent of cold working on the aging properties by a supervised ANN to model the non-linear relationship between processing parameters and the properties. The back-propagation (BP) training algorithm is improved by Levenberg-Marquardt algorithm. A basic repository on the domain knowledge of cold worked aging processes is established via sufficient data mining by the network. The predicted values of the ANN coincide well with the tested data. So an important foundation has been laid for prediction and optimum controlling the rolling and aging properties of Cu-Cr-Zr-Mg alloy.展开更多
This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of ve...This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.展开更多
“Open community” has aroused widespread concern and research. This paper focuses on the system analysis research of the problem that based on statistics including the regression equation fitting function and mathema...“Open community” has aroused widespread concern and research. This paper focuses on the system analysis research of the problem that based on statistics including the regression equation fitting function and mathematical theory, combined with the actual effect of camera measurement method, Prim’s algorithm and neural network to “Open community” and the applicable conditions. Research results show that with the increasing number of roads within the district, the benefit time gradually increased, but each type of district capacity is different.展开更多
Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used ...Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.展开更多
This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated ...This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated by diagnosing the voltage trouble of the fuel injector of an electronic control (EC) engine.展开更多
基金supported by the National Natural Science Foundation of China(6110418440904018)+3 种基金the National Key Scientific Instrument and Equipment Development Project(2011YQ12004502)the Research Foundation of General Armament Department(201300000008)the Doctor Innovation Fund of Naval University of Engineering(HGBSCXJJ2011008)the Youth Natural Science Foundation of Naval University of Engineering(HGDQNJJ12028)
文摘A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and com- pensate for the fiber optic gyro (FOG) bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter (UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise (-20-20℃) and drop (70-20℃) tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation (BP) and UKF network models.
基金Funded by the Zhejiang Provincial Natural Science Foundation of China(No.R405031)Jiaxing Science Planning Project(2009 2007)the Educa-tion Department of Zhejiang Province (No.20051441)
文摘A new thermal ring-opening polymerization technique for 1, 1, 3, 3-tetra-ph enyl-1, 3-disilacyclobutane (TPDC) based on the use of metal nanoparticles produced by pulsed laser ablation was investigated. This method facilitates the synthesis of polydiphenysilylenemethyle (PDPhSM) thin film, which is difficult to make by conventional methods because of its insolubility and high melting point. TPDC was first evaporated on silicon substrates and then exposed to metal nanoparticles deposition by pulsed laser ablation prior to heat treatment.The TPDC films with metal nanoparticles were heated in an electric furnace in air atmosphere to induce ring-opening polymerization of TPDC. The film thicknesses before and after polymerization were measured by a stylus profilometer. Since the polymerization process competes with re-evaporation of TPDC during the heating, the thickness ratio of the polymer to the monomer was defined as the polymerization efficiency, which depends greatly on the technology conditions. Therefore, a well trained radial base function neural network model was constructed to approach the complex nonlinear relationship. Moreover, a particle swarm algorithm was firstly introduced to search for an optimum technology directly from RBF neural network model. This ensures that the fabrication of thin film with appropriate properties using pulsed laser ablation requires no in-depth understanding of the entire behavior of the technology conditions.
基金supported by National High Technical Research and Development Programme of China(No.2002AA331112)supported by the Doctorate Foundation of Northwestern Polytechnical University.
文摘A developmental research has been carried out to deal with the high performance of Cu-Cr-Zr-Mg lead frame alloy by artificial neural network (ANN). Using the cold working to assist in the aging hardening can improve the the hardness and electrical conductivity properties of Cu-Cr-Zr-Mg lead frame alloy. This paper studies the effect of different extent of cold working on the aging properties by a supervised ANN to model the non-linear relationship between processing parameters and the properties. The back-propagation (BP) training algorithm is improved by Levenberg-Marquardt algorithm. A basic repository on the domain knowledge of cold worked aging processes is established via sufficient data mining by the network. The predicted values of the ANN coincide well with the tested data. So an important foundation has been laid for prediction and optimum controlling the rolling and aging properties of Cu-Cr-Zr-Mg alloy.
文摘This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.
文摘“Open community” has aroused widespread concern and research. This paper focuses on the system analysis research of the problem that based on statistics including the regression equation fitting function and mathematical theory, combined with the actual effect of camera measurement method, Prim’s algorithm and neural network to “Open community” and the applicable conditions. Research results show that with the increasing number of roads within the district, the benefit time gradually increased, but each type of district capacity is different.
文摘Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.
文摘This paper introduces the principle for recognition of engine work wave signal with neural network. A diagnosis method for recognizing engine trouble by its work wave is proposed. The designing process is illustrated by diagnosing the voltage trouble of the fuel injector of an electronic control (EC) engine.