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Adaptive Correlation Graph Neural Ordinary Differential Equation for Traffic Flow Forecasting

ENGINEERING LETTERS 2024年第9期32卷 1770-1782页

作者： Bai, Lin Huang, Zheng Wang, Shuang Dai, Hong Univ Sci & Technol Liaoning Anshan 114051 Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Liaoning Peoples R China

Contemporary advancements in deep learning have spurred widespread adoption of spatio-temporal prediction across various scientific disciplines. Nonetheless, traffic flow prediction, as a quintessential spatio-temporal task, continues to present significant challenges, such as the accurate modeling of complex dependencies and dynamic changes over time and space. To address these issues, this paper introduces the Dual-Branch and Multi-Temporal Resolution Convolutional Network with an Adaptive Graph Neural Ordinary Differential Equation (DM-AGODE) model. This innovative approach integrates an optimized graph neural ordinary differential equation with an adaptive correlation adjacency graph, ensuring precise feature propagation across the network. The model incorporates a Dual-Branch Learning (DBL) mechanism to effectively differentiate between short-term dynamics and long-term trends, while the Multi-Temporal Resolution Convolution (MTRC) method enhances the processing of temporal data across multiple scales, critical for capturing the complex behaviors of traffic flow. Furthermore, to demonstrate the effectiveness of our model, we conducted a comprehensive evaluation of our model using six widely recognized real-world datasets, which highlighted its superior adaptability to complex traffic flow patterns. Compared to the leading baseline model, our approach achieves an improvement in prediction accuracy exceeding 8% and significantly enhances efficiency in processing.

关键词： Traffic flow forecasting Multi-step forecasting Neural ODE Spatio-temporal feature fusion training algorithm Dilated Convolution

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Novel associative memory system-based motor saving energy control

Novel associative memory system-based motor saving energy co...

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16th Chinese Control and Decision Conference

作者： Wang Jun-song Tian Ku Cui Shi-gang Liu Yu-min Tianjin Univ Technol & Educ Tianjin 300222 Peoples R China

ISBN: (纸本)9787810545006

Based on the analysis of the saving energy principle of the motor, a novel associative memory system is proposed by applying discrete taylor series, which is capable of implementing error-free approximations to multi-variable polynomial functions of arbitrary order. It offers many advantages over conventional CMAC-type AMS, which are higher-precision of learning, much smaller memory requirement without the data-collision problem, much less computational effort for training and faster convergence rates than that attainable with multi-layer BP neural networks. Then, a new type saving energy control system for the motor is developed, and hardware organization is given. The experiments show that the control system can make the motor work in the better operating state all the time even if the loaded is changed.

关键词： saving energy control discrete taylor series associative memory system interpolation algorithm training algorithm

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A Novel Neural Network Based Modeling for Control of NOx Emission in Power Plant

A Novel Neural Network Based Modeling for Control of NOx Emi...

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2014 2nd Asian Pacific Conference on Mechatronics and Control Engineering(APCMCE 2014)

作者： Pingkang Li Rui Pan Chen Chen Department of Mechanical Electrical and Control Engineering Beijing Jiaotong University

A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides(NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities.

关键词： NOx emission Neural networks training algorithm Modeling

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Wavelet Neural Network with Random Wavelet Function Parameters

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INTERNATIONAL JOURNAL OF ENGINEERING 2017年第10期30卷 1510-1516页

作者： Bazoobandi, H. Esfarayen Univ Technol Dept Comp Engn Esfarayen North Khorasan Iran

The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden layer and output layer of the network;meanwhile, the wavelet function parameters are randomly assigned and kept fixed during the training process. Besides the simplicity and speed of the proposed one-step algorithm, the experimental results verify the performance of the proposed method in terms of final model accuracy and computational time.

关键词： Wavelet Neural Network training algorithm Moore-Penrose Inverse Random Parameter Values

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THE NEURAL NETWORK MODEL BASED ON PSO FOR SHORT-TERM LOAD FORECASTING

THE NEURAL NETWORK MODEL BASED ON PSO FOR SHORT-TERM LOAD FO...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YING-XIA ZHANG WEI SUN FANG-TAO LI Department of Economy and Management North China Electric Power University Baoding 071003 China Department of Engineering Jiangsu Huaneng Huaiyin Power Plant Huai'an 223001 China

A new algorithm for load forecasting--the neural network model based on Particle Swarm Optimization (PSO-NN) for short-term load forecasting is proposed in this paper. The method is simple, easy to realize and its convergence rate is quick. The overall optimal solution of the problem can be found in great probability, and the intrinsic defects of artificial neural network, such as slow training speed and the existence of local minimum points, can be effectively *** results show that forecasting precision and speed can be improved by this method, and its forecasting capability is obviously better than the neural network model based on BP algorithm (BP-NN).

关键词： Load forecasting particle swarm optimization neural network training algorithm

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On Optimal Control Scheme Based on Feedforward and Inverse Models of Artificial Neural Network

On Optimal Control Scheme Based on Feedforward and Inverse M...

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Advances in Information Technology and Education

作者： Dongcai Qu Guorong Zhao Dong Cao Jianhua Lu Binwen Lu Department of Control Engineering of Naval Aeronautical and Astronautical University The Naval Fly Academy

The mostly optimum control questions were based on precise mathematical model of the controlled object at present, and only aimed at the few parameters implementation optimization control. Because it was very difficult or imposable that established precise mathematical model of actual controlled *** actual massive complex, unknown and indefinite nonlinear objects had many optimization parameters, it made conventional optimum control method no longer to be suitable to. In order to solve these problems, in the analysis optimum control principle foundation, the optimum control scheme based on the ANN's(Artificial Neural Network) feedforward / inverse model had designed. Basis on characteristics of nonlinear mapping and adaptive study of ANN's models and so on, the configuration of the ANN's feedforward and inverse models were designed. Used specialized training method of ANN models, and based on the EF(Exponential Forgetting) algorithm renewed covariance matrix, iterative training online to ANN's models were carried out, therefore obtained the ANN's models with the optimization configuration. Simulation results show that, designed optimum control structure scheme is reasonable, and ANN's controller may get good controlling effect.

关键词： Optimum control Neural Network(NN) training algorithm

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Designing Stepped Impedance Microstrip Low-Pass Filters Using Artificial Neural Network at 1.8 GHz

Designing Stepped Impedance Microstrip Low-Pass Filters Usin...

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International Conference on Communication Systems and Network Technologies

作者： Vivek Singh Kushwah Geetam S. Tomar Sarita Singh Bhadauria Dept. of Electronics Amity School of Engineering & Technology Gwalior India Machine Intelligence Research Labs Gwalior 474011 India Dept. of Electronics Madhav Institute of Technology& Science Gwalior India

ISBN: (纸本)9781467356039

In this paper a design technique for a Stepped impedance Microstrip Low-pass filters is presented by using the artificial neural network (ANN) modeling method. Required dimensions of the microstrip filter layout are used to get the input-output relationships in the ANN model. This paper presents the design and analysis of Stepped impedance Microstrip Low-pass filters at cut-off frequency 1.8 GHz which gives insertion loss (S_(21)) of -3 dB at cut-off point. Subsequently an artificial neural network model is developed to find out the Magnitude variation of scattering parameters (S-parameters) of Microstrip Low-pass filters at 1.8 GHz for different dimensions. After developing the ANN model of microstrip low-pass filter, it is computationally more efficient in the design and it has been displayed to be as accurate as an Electromagnetic simulator. The simulation is performed using the commercial software IE3D 14.1 and MATLAB programming language GUI Tools are used for ANN training.

关键词： Microstrip Low Pass Filters Lumped elements ANN model MATLAB IE3D EM Simulation S-parameters training algorithm

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Temperature Error Compensation for Open-Loop Fiber Optical Gyro using Back-Propagation Neural Networks with Optimal Structure

Temperature Error Compensation for Open-Loop Fiber Optical G...

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2014中国制导、导航与控制学术会议

作者： Limei Song Wei Zhang AVIC XI''AN Fight Automatic Control Research Institute Xi''an710065 China Delegation of Air ForceXi''an710065 China

ISBN: (纸本)9781479946983

Open-Loop Fiber Optic Gyroscopes (FOG) is widely used,which is easily affected by the temperature around *** temperature model has a very complicated nonlinear characteristic.A BP neural network model with advantage of approximating the nonlinear function was developed to simulate outputs of an open-loop FOG and then compensate the FOG's temperature error in full temperature range??–50??~ +70????.With experimental data,the networks with one-hidden-layer structure adopted the temperature and the temperature change rate as network inputs,and the outputs of FOG as network *** results showed that the number of hidden-layer neurons plays an important role in simulation performance,and the network with 11 hidden-layer neurons offered better precision and ***,the comparison of 4 different training algorithms demonstrated that the Levenberg-Marquardt algorithm resulted in a better convergence during training *** the chosen structure and training algorithm,the BP neural network model was used to compensate the temperature error of the *** was found that the compensated outputs of the FOG became more accurate and more *** addition,the neural network model further proved its superiority of precision and robustness by comparison with a multiple linear regression model and a quadratic curve fitting model.

关键词： backpropagation computerised instrumentation curve fitting error compensation fibre optic gyroscopes neural nets regression analysis BP neural network model FOG Levenberg-Marquardt algorithm backpropagation neural network model hidden-layer neuron multiple linear regression model nonlinear function approximation one-hidden-layer structure open-loop fiber optical gyroscope optimal structure quadratic curve fitting model temperature error compensation training algorithm Biological neural networks Neurons Stability analysis Temperature distribution Temperature measurement Thermal stability training

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Use of videolaryngoscopy as the first option for all tracheal intubations: technical benefits and a simplified algorithm for airway management

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BRITISH JOURNAL OF ANAESTHESIA 2023年第4期130卷 E425-E426页

作者： Penketh, James Kelly, Fiona E. Cook, Timothy M. Royal United Hosp Bath NHS Fdn Trust Dept Anaesthesia & Intens Care Med Bath Somerset England

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A High-Efficiency Surrogate Model Based on BP Neural Network

A High-Efficiency Surrogate Model Based on BP Neural Network

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第四届国际航空科学青年学者学术会议

作者： ZHANG Xin YANG Muqing SHAN Lin School of Aeronautic Science and Engineering Beijing University of Aeronautics and Astronautics School of Management University of Liverpool

In order to predict the aerodynamic characteristics of airfoils more accurately,an efficient surrogate model PSO-BP is proposed in this paper based on Backpropagation(BP) neural *** PSO-BP utilizes the particle swarm optimization(PSO) to optimize the training algorithm and topology of the BP network,and then adopts Bayesian regularization to strengthen its generalization *** results indicate that the PSO-BP offers higher prediction accuracy(within ±5%) and stronger robustness compared to the other four models,including the standard BP neural network,Response Surface Model,Kriging approximation and Radial Basis Function approximation.

关键词： neural network training algorithm Bayesian regularization accuracy robustness

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