pH regulation is a complicated and comprehensive technique in the crop fertigation system. In this paper, a method is put forward to improve the quality of pH regulation, using artificial neural network to map a nonli...pH regulation is a complicated and comprehensive technique in the crop fertigation system. In this paper, a method is put forward to improve the quality of pH regulation, using artificial neural network to map a nonlinear relationship between pH interfering factor and the switching frequency of pH control valve, which achieves the dynamic feedforward compensation to the main control system.展开更多
An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the err...An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.展开更多
A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The prin...A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.展开更多
Many methods to calculate the consolidation coefficient of soil depend on judgment of testing curves of consolidation, and the calculation result is influenced by artificial factors. In this work, based on the main pr...Many methods to calculate the consolidation coefficient of soil depend on judgment of testing curves of consolidation, and the calculation result is influenced by artificial factors. In this work, based on the main principle of back propagation neural network, a neural network model to determine the consolidation coefficient is established. The essence of the method is to simulate a serial of compression ratio and time factor curves because the neural network is able to process the nonlinear problems. It is demonstrated that this BP model has high precision and fast convergence. Such method avoids artificial influence factor successfully and is adapted to computer processing.展开更多
Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.
In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is c...In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.展开更多
Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the curr...Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.展开更多
文摘pH regulation is a complicated and comprehensive technique in the crop fertigation system. In this paper, a method is put forward to improve the quality of pH regulation, using artificial neural network to map a nonlinear relationship between pH interfering factor and the switching frequency of pH control valve, which achieves the dynamic feedforward compensation to the main control system.
文摘An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.
文摘A DRNN (diagonal recurrent neural network) and its RPE (recurrent prediction error) learning algorithm are proposed in this paper .Using of the simple structure of DRNN can reduce the capacity of calculation. The principle of RPE learning algorithm is to adjust weights along the direction of Gauss-Newton. Meanwhile, it is unnecessary to calculate the second local derivative and the inverse matrixes, whose unbiasedness is proved. With application to the extremely short time prediction of large ship pitch, satisfactory results are obtained. Prediction effect of this algorithm is compared with that of auto-regression and periodical diagram method, and comparison results show that the proposed algorithm is feasible.
文摘Many methods to calculate the consolidation coefficient of soil depend on judgment of testing curves of consolidation, and the calculation result is influenced by artificial factors. In this work, based on the main principle of back propagation neural network, a neural network model to determine the consolidation coefficient is established. The essence of the method is to simulate a serial of compression ratio and time factor curves because the neural network is able to process the nonlinear problems. It is demonstrated that this BP model has high precision and fast convergence. Such method avoids artificial influence factor successfully and is adapted to computer processing.
文摘Based on the statistical data from 1975 to 1997, we forecast the growth rate of coal consuming and the quantity in coming decade with the BP neuron network in the article.
基金supported by the National Natural Science Foundation of China(No. 61671237)the Foundation of State Key Laboratory of Air Traffic Management System and Technology(No. SKLATM202003)the Fundamental Research Funds for Graduates of Nanjing University of Aeronautics and Astronautics (No. kfjj20200735)
文摘In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.
基金Supported by the Postdoctoral Science Foundation of China（ No. 20100480964 ） , the Basic Research Foundation of Central University （ No. HEUCF100104） and the National Natural Science Foundation of China （No. 50909025/E091002）.
文摘Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.
