This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in boa...This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.展开更多
An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer no...An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.展开更多
To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are anal...To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.展开更多
The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the m...The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the models to predict volume content of composition in particie reinforced ceramies are established. The Al2O3/TiN ceramie cutting tool material was developed by ANN, whose mechanicai properties fully satisfy the cutting requirements.展开更多
A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificia...A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[展开更多
Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural net...Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.展开更多
The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the ...The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.展开更多
This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the st...This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.展开更多
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.展开更多
The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires preci...The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models.However,such processes are limited to known system models,which are uncertain in future systems.Here,the controller for 3-phase qZS-CMI is modeled based on two phases,and the source PV voltage and output grid current are controlled.In Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid current.For two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is used.Moreover,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid current.The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm.The main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid current.Hence,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.展开更多
According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved BP...According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved BP network algorithm. The optimum ignition advance angleand fuel injection pulse band of engine under different speed and load are tested for the samplestraining network, focusing on the study of the design method and procedure of BP neural network inengine injection and ignition control. The results show that artificial neural network technique canmeet the requirement of engine injection and ignition control. The method is feasible for improvingpower performance, economy and emission performances of gasoline engine.展开更多
A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without req...A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.展开更多
The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is...The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.展开更多
基金This paper is supported by the Nature Science Foundation of Heilongjiang Province.
文摘This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.
基金Supported by Foundation for University Key Teacher by Ministryof Education.
文摘An artificial neural network (ANN) short term forecasting model of consumption per hour was built based on seasonality,trend and randomness of a city period of time water consumption series.Different hidden layer nodes,same inputs and forecasting data were selected to train and forecast and then the relative errors were compared so as to confirm the NN structure.A model was set up and used to forecast concretely by Matlab.It is tested by examples and compared with the result of time series trigonometric function analytical method.The result indicates that the prediction errors of NN are small and the velocity of forecasting is fast.It can completely meet the actual needs of the control and run of the water supply system.
文摘To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
文摘The artificial neural networks (ANN) which have broad application were proposed to develop multiphase ceramie cutting tool materials. Based on the back propagation algorithm of the forward multilayer perceptron, the models to predict volume content of composition in particie reinforced ceramies are established. The Al2O3/TiN ceramie cutting tool material was developed by ANN, whose mechanicai properties fully satisfy the cutting requirements.
文摘A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[
基金Item Sponsored by National Natural Science Foundation of China (60277029)
文摘Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （BP） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the BP network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of BP algorithm on the classification of the surface defects of steel plate.
基金This work was supported by the youth backbone teachers training program of Henan colleges and universities under Grant No.2016ggjs-287the project of science and technology of Henan province under Grant No.172102210124the Key Scientific Research projects in Colleges and Universities in Henan(Grant No.18B460003).
文摘The heating technological requirement of the conventional PID control is difficult to guarantee which based on the precise mathematical model,because the heating furnace for heating treatment with the big inertia,the pure time delay and nonlinear time-varying.Proposed one kind optimized variable method of PID controller based on the genetic algorithm with improved BP network that better realized the completely automatic intelligent control of the entire thermal process than the classics critical purporting(Z-N)method.A heating furnace for the object was simulated with MATLAB,simulation results show that the control system has the quicker response characteristic,the better dynamic characteristic and the quite stronger robustness,which has some promotional value for the control of industrial furnace.
文摘This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.
文摘Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
文摘The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter(qZSCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this paper.The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models.However,such processes are limited to known system models,which are uncertain in future systems.Here,the controller for 3-phase qZS-CMI is modeled based on two phases,and the source PV voltage and output grid current are controlled.In Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid current.For two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is used.Moreover,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid current.The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm.The main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid current.Hence,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.
文摘According to advantages of neural network and characteristics of operatingprocedures of engine, a new strategy is represented on the control of fuel injection and ignitiontiming of gasoline engine based on improved BP network algorithm. The optimum ignition advance angleand fuel injection pulse band of engine under different speed and load are tested for the samplestraining network, focusing on the study of the design method and procedure of BP neural network inengine injection and ignition control. The results show that artificial neural network technique canmeet the requirement of engine injection and ignition control. The method is feasible for improvingpower performance, economy and emission performances of gasoline engine.
文摘A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.
基金This project is supported by Foundation of Public Laboratory on Robotics of Chinese Academy of Sciences.
文摘The pneumatic artificial muscles are widely used in the fields of medicalrobots, etc. Neural networks are applied to modeling and controlling of artificial muscle system. Asingle-joint artificial muscle test system is designed. The recursive prediction error (RPE)algorithm which yields faster convergence than back propagation (BP) algorithm is applied to trainthe neural networks. The realization of RPE algorithm is given. The difference of modeling ofartificial muscles using neural networks with different input nodes and different hidden layer nodesis discussed. On this basis the nonlinear control scheme using neural networks for artificialmuscle system has been introduced. The experimental results show that the nonlinear control schemeyields faster response and higher control accuracy than the traditional linear control scheme.
