检索结果-内蒙古大学图书馆

Application of BPANN for prediction of backward ball spinning of thin-walled tubular part with longitudinal inner ribs

引用

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2008年第1-3期196卷 190-196页

作者： Jiang, Shuyong Ren, Zhengyi Xue, Kemin Li, Chunfeng Harbin Engn Univ Engn Training Ctr Harbin 150001 Peoples R China Hefei Univ Technol Sch Mat Sci & Engn Hefei 230000 Peoples R China Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

As a successively and locally plastic deformation process, backward ball spinning is applied for the purpose of manufacturing thin-walled tubular part with longitudinal inner ribs. Obtaining the desired inner ribs is one of the most critical tasks in backward ball spinning of thin-walled tubular part with longitudinal inner ribs and the formability of inner ribs depends greatly on the process parameters, such as ball diameter, feed ratio, wall thickness reduction and wall thickness of tubular blank. As a nonlinear dynamics system simulating structure and function of biological neural network in the human brain, back-propagation artificial neural network (BPANN) is used in backward ball spinning of thin-walled tubular part with longitudinal inner ribs. The attractiveness of BPANN comes from its remarkable information processing characteristics pertinent mainly to nonlinearity, adaptability, high parallelism, learning capability, fault and noise tolerance so that it can be more efficient in solving complex and nonlinear optimization problems in backward ball spinning of thin-walled tubular parts with longitudinal inner ribs. Not only can BPANN successfully predict the formability of the inner ribs, but it can simulate the influences of the process parameters on the height of inner ribs as well. In the end, the process parameters are matched so rationally that the desired spun parts can be obtained. (c) 2007 Elsevier B.V. All rights reserved.

关键词： power spinning ball spinning artificial neural networks longitudinal inner ribs back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Estimating the fracture gradient coefficient using neural networks for a field in the Middle East

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2005年第3-4期49卷 193-211页

作者： Malallah, A Nashawi, IS Coll Engn & Petr Dept Petr Engn Safat 13060 Kuwait

Fracture gradient constitutes an essential parameter in the pre-design stage of drilling operations, reservoir exploitation and stimulation. Over the years, fracture gradient has been acknowledged to be one of the most important key element that needs to be accurately determined in order to achieve optimum operation results. Several calculation methods have been presented in the literature for different regions of the world. All of these techniques were based on either parametric or empirical correlations, which require a prior knowledge of the functional form. Moreover, these methods are only valid at the area where they were developed. This paper presents an artificial neural network model to predict the fracture gradient for the Middle East region. The data set used in the study consists of more than 21,000 points with fracture gradient measurements taken from 16 wells drilled in seven geological prospects covering more than 200 sq mi. The excellent results obtained from the developed model establish a new simple mechanism for fracture gradient prediction that is based on readily obtainable parameters. The proposed neural network model predicts the fracture gradient as a function of pore pressure, depth and rock density. A detailed comparison between the results predicted by this method and those predicted by other techniques is presented. (c) 2005 Elsevier B.V. All rights reserved.

关键词： artificial neural network back-propagation algorithm feed-forward fracture gradient pore pressure gradient

来源：评论

学校读者我要写书评

暂无评论

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

引用

PRAMANA-JOURNAL OF PHYSICS 2011年第6期76卷 887-900页

作者： Kadir, Abdurahman Wang, Xing-Yuan Zhao, Yu-Zhang Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Xinjiang Univ Finance & Econ Sch Engn & Comp Sci Urumqi 830012 Peoples R China

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

关键词： Chaos control adaptive control adaptive identifier fuzzy neural network back-propagation algorithm

来源：评论

学校读者我要写书评

暂无评论

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2021年第7期235卷 1179-1192页

作者： Imrane, Mahamat Loutfi Melingui, Achille Ahanda, Joseph Jean Baptiste Mvogo Motto, Frederic Biya Merzouki, Rochdi Univ Yaounde I Dept Phys Fac Sci Yaounde Cameroon Univ Yaounde I Dept Elect & Telecommun Engn Ecole Natl Super Polytech Yaounde 8390 Cameroon Univ Bamenda Dept Elect & Power Engn Bamenda Cameroon Univ Lille CRIStAL Lab CNRS UMR Nord France

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots' autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller's outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system's efficiency.

关键词： Mobile robots autonomous navigation type-2 fuzzy logic neural networks back-propagation algorithm artificial potential field

来源：评论

学校读者我要写书评

暂无评论

Applying Artificial Neural Networks to Modeling the Middle Atmosphere

引用

Advances in Atmospheric Sciences 2010年第4期27卷 883-890页

作者：肖存英胡雄 Center for Space Science and Applied Research Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

An artificial neural network （ANN） is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propagation （BP） algorithm. Latitude, longitude, and height are chosen as the input vectors of the network while temperature is the output vector. The temperature observations during the period from 13 January through 16 March 2007, which are in the same satellite yaw, are taken as samples to train an ANN. Results suggest that the network has high quality for modeling spatial variations of temperature. Quantitative comparisons between the ANN outputs and those from the popular empirical NRLMSISE-00 model illustrate their generally consistent features and some specific differences. The NRLMSISE-00 model＇s zonal mean temperatures are too high by ～6 K-10 K near the stratopause, and the amplitude and phase of the planetary wave number 1 activity are different in some respects from the ANN simulations above 45-50 km, suggesting improvement is needed in the NRLMSISE-00 model for more accurate simulation near and above the stratopause.

关键词： artificial neural network middle atmosphere modeling back-propagation algorithm NRLMSISE- 00 model

来源：评论

学校读者我要写书评

暂无评论

An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process

引用

ARTIFICIAL INTELLIGENCE IN ENGINEERING 1999年第2期13卷 131-140页

作者： Liu, W Hebei Inst Technol Dept Automat Engn Tangshan City 063009 Hebei Peoples R China

The purpose of this paper is to present results that were obtained in fault diagnosis of glutamic acid fermentation process. The diagnosis algorithm is based on the extended Kalman filter (EKF) and neural network classifier. Inputs of the network are the process I/O data, such as pressure and temperature, parameters estimated by EKF, and state values calculated by dynamic equations, while outputs of the network are process fault situations. A batch glutamic acid fermentation process is studied as a test east, which is with 13 measurements, five estimated parameters, three calculated states, and 11 fault situations. The running test results show that the strategy appears to be better suited to diagnose faults of such an industrial process. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： neural networks fault diagnosis classification back-propagation algorithm extended Kalman filter fermentation process

来源：评论

学校读者我要写书评

暂无评论

Extreme learning machine: Theory and applications

引用

NEUROCOMPUTING 2006年第1-3期70卷 489-501页

作者： Huang, Guang-Bin Zhu, Qin-Yu Siew, Chee-Kheong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.(I) (c) 2006 Elsevier B.V. All rights reserved.

关键词： feedforward neural networks back-propagation algorithm extreme learning machine support vector machine real-time learning random node

来源：评论

学校读者我要写书评

暂无评论

Neural network-based expert system for modeling of tube spinning process

引用

NEURAL COMPUTING & APPLICATIONS 2015年第6期26卷 1481-1493页

作者： Vundavilli, Pandu R. Kumar, J. Phani Priyatham, Ch. Sai Parappagoudar, Mahesh B. IIT Bhubaneswar Sch Mech Sci Bhubaneswar 751013 Orissa India DVR & Dr HS MIC Coll Technol Dept Mech Engn Kanchikacherla 521180 AP India Chhatrapati Shivaji Inst Technol Dept Mech Engn Durg 491001 CG India

The present paper deals with the development of neural network (NN)-based expert system for modeling of 2024 aluminum tube spinning process. Tube spinning is a highly nonlinear thermo-mechanical process for producing large-diameter thin-walled shapes. It is interesting to note that the performance of the process depends on various process parameters, such as wall thickness, percentage of thickness reduction, feed rate, mandrel rotational speed, solution treatment time and aging time. Therefore, an NN-based expert system is necessary for modeling the tube spinning process. The input layer of NN consists of six neurons corresponding to the inputs of the tube spinning process. Moreover, the output layer consists of four neurons that represent four responses, namely change in diameter, change in thickness, inner and outer surface roughness. It is to be noted that the performance of NN depends on various factors, such as number of neurons in the hidden layer, coefficients of transfer functions and connecting weights, etc. In the present paper, three algorithms, such as back-propagation, genetic and artificial bee colony algorithms, are used for optimizing the said variables of NN. Further, the developed approaches are tested for their accuracy in prediction with the help of some test cases and found to model the tube spinning process effectively.

关键词： Tube spinning Neural networks back-propagation algorithm Genetic algorithm Artificial bee colony

来源：评论

学校读者我要写书评

暂无评论

Estimation of axial DNBR distribution at the hot pin position of a reactor core using fuzzy neural networks

引用

JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY 2004年第8期41卷 817-826页

作者： Na, MG Shin, SH Lee, SM Jung, DW Lee, K Lee, YJ Chosun Univ Dept Nucl Engn Kwangju 501759 South Korea Korea Atom Energy Res Inst Taejon 305600 South Korea

The pressurized water reactor (PWR) generally operates in the forced convection or nucleate boiling regime. However, if the fuel rod is operating at a high power density, the nucleate boiling that is characterized by extremely high heat transfer rates becomes film boiling with severely reduced heat transfer capability, which is called Departure from Nucleate Boiling (DNB). In this work, the axial DNB Ratio (DNBR) distribution at the hot pin position is predicted by the fuzzy neural networks using the measured signals of the reactor coolant system. The fuzzy neural network is a fuzzy inference system equipped with a training algorithm. The fuzzy neural network is trained by a hybrid method combined with a back-propagation algorithm and a least-squares algorithm. The proposed method is applied to the first cycle of the Yonggwang 3 nuclear power plant. The relative 2-sigma error averaged for 13 axial locations of the hot rod is 1.97%. The fuzzy neural networks estimate DNBRs more accurately at central parts that have relatively lower DNBR values which are more important in safety aspects. From these simulation results, it is known that this algorithm can provide reliable protection and monitoring information for the nuclear power plant operation and diagnosis by accurately predicting the DNBR each time step.

关键词： departure from nucleate boiling (DNB) DNBR estimation fuzzy neural networks back-propagation algorithm least-squares method

来源：评论

学校读者我要写书评

暂无评论

Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal

引用

JOURNAL OF MEMBRANE SCIENCE 2005年第1-2期248卷 53-62页

作者： Aydiner, C Demir, I Yildiz, E Gebze Inst Technol Dept Environm Engn TR-41400 Kocaeli Turkey Ataturk Univ Fac Engn Dept Environm Engn TR-25240 Erzurum Turkey

Flux declines versus time (t) during crossflow microfiltration of a mixture that contains phosphate and fly ash, were modeled and compared by using an artificial neural network (ANN) and Koltuniewicz's method (KM) at different conditions of fly ash dosages (C-FA), PO4 concentrations (C-PO4), transmembrane pressures (DeltaP) and two membrane types (M-T). Two different neural network (NN) architectures (NN1, NN2) that gave the best prediction of flux values were established for data analyses. KM was also taken as Kl and K2 to compare the predictions of both models at the same experiments separately. It was shown that all of the experimental conditions can be modeled as a whole or separately, and the model results obtained for one experiment can be used for others at the same conditions with an acceptable correlation level by NNs while not with Koltuniewicz. The correlation values were found as 0.991 and 0.988 for NN1 and NN2, and 0.972 and 0.973 for Kl and K2, respectively. These results are put forward to show that ANN results fit better to fluxes than KM according to correlation values (r(2)). The normalized flux values obtained from K1 and K2, smaller than 0.4, are in the range between -30 and 40% variations, whereas the most of N1 and N2 variations are in the range of +/-20%. The error distributions of data used for NN1 and NN2 were found to be 82 and 79%, while for K1 and K2 was calculated to be 51 and 52%, in the range of +/-10% error, respectively. The contribution of t variable to flux values provided by NNs was determined in an important level at the range of 40-50% due to increasing in membrane fouling by the time. The contributions of DeltaP, C-FA and C-PO4 variables were found in the range of 15-25%. The affect of MT was determined at a lower level about for 4%. As a conclusion, using elaborated ANN modeling, it is able to predict the permeate flux at a high accuracy from process variables such as transmembrane pressure, various concentrations of feed solu

关键词： modeling of flux decline neural networks Koltuniewicz back-propagation algorithm crossflow microfiltration

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：