检索结果-内蒙古大学图书馆

Machine Learning-Based Sizing Model for Tapered Electrical Submersible Pumps Under Multiple Operating Conditions

PROCESSES 2025年第4期13卷

作者： Yao, Jinsong Han, Guoqing Liang, Xingyuan Wang, Mengyu China Univ Petr Coll Petr Engn Beijing 102249 Peoples R China

Dewatering gas wells typically exhibit a high gas-liquid ratio, making tapered electrical submersible pump (ESP) systems a common choice. However, the flow rate within the pump varies significantly along its length, and production parameters fluctuate considerably across different stages of operation for a gas reservoir. Traditional ESP sizing methods typically consider one single operating case and one single pump model. In contrast, tapered ESP systems require the designer to manually select and combine pump models, stage numbers, and operating frequencies based largely on experience. This process can be cumbersome and time-consuming. To address the limitations of existing ESP sizing methods, this study develops a computational program for ESP operation parameters stage by stage and generates extensive training data. A fully connected neural network (FCNN) based on the backpropagation (BP) algorithm is then trained on these data. The model can predict key parameters such as gas volume fraction (GVF) and flow rate along the pump, operating frequency, and total pump efficiency, using input data such as fluid parameters at the pump's intake and discharge, as well as pump stage numbers and performance curve data. The model demonstrates high accuracy, with a mean absolute error (MAE) of 0.3431, a mean squared error (MSE) of 0.3231, and a coefficient of determination (R2) of 0.9991. By integrating a wellbore two-phase flow model and leveraging industry experience in pump sizing, a hybrid model for automatic ESP sizing under multiple working conditions is proposed, with the objective of maximizing pump efficiency. This model enables optimal pump sizing, calculates the operating frequency corresponding to given working cases, significantly reduces the workload of designers, and enhances the overall design outcomes.

关键词： tapered ESP system backpropagation algorithm fully connected neural network hybrid model pump efficiency ESP sizing multiple working conditions

来源：评论

学校读者我要写书评

暂无评论

Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction

引用

INFORMATION SCIENCES 2015年 325卷 159-174页

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey-Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey-Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Neural network Generalized type-2 fuzzy weight backpropagation algorithm Generalized type-2 fuzzy system

来源：评论

学校读者我要写书评

暂无评论

A method for suppressing spectrum aliasing of off-axis digital holograms based on Kronecker interpolation and backpropagation

引用

OPTICS AND LASERS IN ENGINEERING 2024年 175卷

作者： Guan, Ying Cui, Ze Xu, Zihan Zhou, Wenjing Shanghai Univ Dept Precis Mech Engn Shanghai 200072 Peoples R China

The research proposes a new method to solve the spectrum aliasing and improve the resolution of off-axis digital holograms based on Kronecker interpolation and backpropagation algorithms. The method suppresses the direct current (DC) image and increase the low-frequency information with Kronecker interpolation of the hologram. The size of the Fresnel reconstructed image can be controlled with the reconstruction distance, and this feature is used to select the appropriate reconstruction distance for the Fresnel amplitude reconstruction of interpolated holograms to separate the real and imaginary images. To obtain the complete first-order spectral information for the object, the amplitude of the desired image is obtained with spatial domain filtering and then inverted. Finally, the amplitude and phase of the object are reconstructed according to the angular spectrum reconstruction algorithm. The results show that the method can solve the spectrum aliasing problem of the holograms and thus improve the resolution of off-axis holograms. In addition, the background noise of the reconstructed phase is reduced because the influence of the zero-order spectrum can be completely avoided.

关键词： Kronecker interpolation Off -axis digital holography backpropagation algorithm Spectrum aliasing

来源：评论

学校读者我要写书评

暂无评论

EXPERIMENTATION WITH A backpropagation NEURAL NETWORK - AN APPLICATION TO PLANNING END USER SYSTEM-DEVELOPMENT

引用

INFORMATION & MANAGEMENT 1993年第1期24卷 1-8页

作者： LODEWYCK, RW DENG, PS CALIF STATE UNIV STANISLAUS TURLOCKCA

Neural networks utilize the paradigm of the human brain to acquire knowledge through a training process. The knowledge base is stored in the form of weighted interconnections between layer nodes;this allows the network to generalize. Computational experimentation with a back-propagation algorithm yielded useful information concerning the effect of several key model parameters on network training and performance. These findings are illustrated through the application of a modest sized three layer neural network to the planning of end user involvement in the development of information systems.

关键词： backpropagation algorithm NEURAL NETWORKS CONNECTIONIST MODELS UNSTRUCTURED DECISION MAKING END USER SYSTEM DEVELOPMENT INFORMATION SYSTEM PLANNING

来源：评论

学校读者我要写书评

暂无评论

backpropagation - Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom

引用

SAFETY SCIENCE 2020年 122卷

作者： Amin, Shohel Coventry Univ Sch Energy Inst Future Transport & Cities Construct & Environm Sir John Laing Bldg Coventry CV1 5FB W Midlands England

Older people are vulnerable road users with higher rate of casualties in traffic accidents. The commonly cited causes of accidents for older people are poor attention and decision making at critical locations of road, poor visibility in extreme weather, poor road surface condition and unpredictability of other road users, particularly young drivers. Female drivers are often labelled as being precarious drivers and having higher accident risk comparing to male drivers. This paper applies backpropagation - Artificial Neural Network (BP-ANN) with a Generalized Delta Rule (GDR) learning algorithm to model the factors affecting traffic accidents of both older female and male drivers. The BP-ANN can construct the causation model of traffic accidents with greater accuracy and define the proportion of errors contributed by each factor to traffic accidents. This paper studies a total of 95,092 accident records in West Midlands of the United Kingdom during the period of 2006 to 2016. This paper determines journey purpose, lighting condition, pedestrian crossing with physical interventions, complex roadway geometry, extreme weather and time severity as the most significant factors of older driver accidents. The accident risk of older drivers can be improved by providing accessible routes, affordable, reliable and convenient public transport, timely warning of unexpected situations and changes in roadway geometry;increasing use of assistive technology in cars, driverless cars and encouraging active transports into sociable activities. The findings help the transport authorities and city councils to develop strategies and measures promoting public and active transports to ensuring the safety of older drivers.

关键词： Accident Gender Older drivers Neural network backpropagation algorithm Modelling errors

来源：评论

学校读者我要写书评

暂无评论

An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity

引用

NEUROCOMPUTING 2017年 237卷 193-199页

作者： Negrov, D. Karandashev, I. Shakirov, V. Matveyev, Yu. Dunin-Barkowski, W. Zenkevich, A. Moscow Inst Phys & Technol Lab Funct Mat & Devices Nanoelect Dolgoprudnyi Russia Russian Acad Sci Sci Res Inst Syst Anal Moscow Russia Natl Res Nucl Univ MEPhI Moscow Russia

We describe an approximation to backpropagation algorithm for training deep neural networks, which is designed to work with synapses implemented with memristors. The key idea is to represent the values of both the input signal and the backpropagated delta value with a series of pulses that trigger multiple positive or negative updates of the synaptic weight, and to use the min operation instead of the product of the two signals. In computational simulations, we show that the proposed approximation to backpropagation is well converged and may be suitable for memristor implementations of multilayer neural networks.

关键词： Deep learning Memristor Neural networks Hardware design backpropagation algorithm

来源：评论

学校读者我要写书评

暂无评论

A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition

引用

NEUROCOMPUTING 2016年 193卷 92-105页

作者： Murru, Nadir Rossini, Rosaria Univ Turin Dept Math Via Carlo Alberto 8-10 Turin Italy

Convergence rate of training algorithms for neural networks is heavily affected by initialization of weights. In this paper, an original algorithm for initialization of weights in backpropagation neural net is presented with application to character recognition. The initialization method is mainly based on a customization of the Kalman filter, translating it into Bayesian statistics terms. A metrological approach is used in this context considering weights as measurements modeled by mutually dependent normal random variables. The algorithm performance is demonstrated by reporting and discussing results of simulation trials. Results are compared with random weights initialization and other methods. The proposed method shows an improved convergence rate for the backpropagation training algorithm. (C) 2016 Elsevler B.V. All rights reserved.

关键词： backpropagation algorithm Bayesian statistics Character recognition Kalman filter Neural network

来源：评论

学校读者我要写书评

暂无评论

Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks

引用

INFORMATION SCIENCES 2022年 600卷 94-117页

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Cheah, Wooi Ping Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Bukit Beruang Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Taikang East Rd Ningbo 315100 Peoples R China

In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN;(2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. PSO utilizes a multi-objective function to improve the effectiveness of the proposed method. In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. A set of 16 real-world CNs in a range from small to large size are used to verify the effectiveness and efficiency of the method in a benchmark study. The proposed method is implemented in multi-machines with central processing unit (CPU) and graphics processing unit (GPU) devices. The results reveal the effective role of the proposed deep learning with hybrid BP-PSO optimization in detecting communities in large CNs, which requires minimum execution time on both CPU and GPU devices.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Community detection Deep learning Distributed and parallel computing Particle swarm optimization backpropagation algorithm Complex networks

来源：评论

学校读者我要写书评

暂无评论

Neural Network with Lower and Upper Type-2 Fuzzy Weights using the backpropagation Learning Method

Neural Network with Lower and Upper Type-2 Fuzzy Weights usi...

引用

Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479903481

In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

来源：评论

学校读者我要写书评

暂无评论

An Analysis of MVL Neural Operators Using Feed Forward backpropagation: Realization and Application of Logic Synthesis 1

An Analysis of MVL Neural Operators Using Feed Forward Backp...

引用

International Conference on Smart Smart Sensors and Applications

作者： Chowdhury, Adib Kabir Razali, Md. Saifullah Wyai, Gary Loh Chee Gopal, Lenin Madon, Bakri Singh, Ashutosh Kumar Univ Coll Technol Sarawak Sch Comp Sibu Malaysia Curtin Univ Sarawak Dept Elect & Comp Engn Miri Malaysia NIT Dept Comp Applicat Kurukshetra Haryana India

ISBN: (纸本)9781479973644

In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated by accuracy measurement of MVL neural operator realization. Furthermore, evaluation of NND-MVL algorithm is analyzed by its application, propagation delay and accuracy achieved for training with 4 hidden neurons. In a brief, an effort of training MVL neural operators and utilizing them for logic synthesis is observed.

关键词： MVL functions learning capability backpropagation algorithm synthesis of functions neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：