Adv. Artif. Neural Syst.最新文献_第5页

Neural Virtual Sensors for Adaptive Magnetic Localization of Autonomous Dataloggers 自主数据采集器自适应磁定位的神经虚拟传感器

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/394038

Dennis Groben, K. Thongpull, A. C. Kammara, A. König

{"title":"Neural Virtual Sensors for Adaptive Magnetic Localization of Autonomous Dataloggers","authors":"Dennis Groben, K. Thongpull, A. C. Kammara, A. König","doi":"10.1155/2014/394038","DOIUrl":"https://doi.org/10.1155/2014/394038","url":null,"abstract":"The surging advance in micro - and nanotechnologies allied with neural learning systems allows the realization of miniaturized yet extremely powerful multisensor systems and networks for wide application fields, for example, in measurement, instrumentation, automation, and smart environments. Time and location context is particularly relevant to sensor swarms applied for distributed measurement in industrial environment, such as, for example, fermentation tanks. Common RF solutions face limits here, which can be overcome by magnetic systems. Previously, we have developed the electronic system for an integrated data logger swarm with magnetic localization and sensor node timebase synchronization. The focus of this work is on an approach to improving both localization accuracy and flexibility by the application of artificial neural networks applied as virtual sensors and classifiers in a hybrid dedicated learning system. Including also data from an industrial brewery environment, the best investigated neural virtual sensor approach has achieved an advance in localization accuracy of a factor of 4 compared to state-of-the-art numerical methods and, thus, results in the order of less than 5 cm meeting industrial expectations on a feasible solution for the presented integrated localization system solution.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"30 1","pages":"394038:1-394038:17"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77724167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Virtual Sensor for Calibration of Thermal Models of Machine Tools 用于机床热模型标定的虚拟传感器

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/347062

A. Dementjev, Burkhard Hensel, K. Kabitzsch, B. Kauschinger, Steffen Schröder

{"title":"Virtual Sensor for Calibration of Thermal Models of Machine Tools","authors":"A. Dementjev, Burkhard Hensel, K. Kabitzsch, B. Kauschinger, Steffen Schröder","doi":"10.1155/2014/347062","DOIUrl":"https://doi.org/10.1155/2014/347062","url":null,"abstract":"Machine tools are important parts of high-complex industrial manufacturing. Thus, the end product quality strictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"82 4","pages":"347062:1-347062:10"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91421577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm 基于混合模糊小波神经网络和基于教学的优化算法的心脏病诊断

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/796323

J. Alneamy, Rahma Abdulwahid Hameed Alnaish

{"title":"Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm","authors":"J. Alneamy, Rahma Abdulwahid Hameed Alnaish","doi":"10.1155/2014/796323","DOIUrl":"https://doi.org/10.1155/2014/796323","url":null,"abstract":"Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithmand fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithmis applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"1 1","pages":"796323:1-796323:11"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89848333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Optimal Design of PID Controller for the Speed Control of DC Motor by Using Metaheuristic Techniques 基于元启发式技术的直流电动机速度PID控制器优化设计

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/126317

M. M. Sabir, J. A. Khan

引用次数: 50

Downscaling Statistical Model Techniques for Climate Change Analysis Applied to the Amazon Region 亚马逊地区气候变化分析的降尺度统计模型技术

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/595462

D. Mendes, J. Marengo, Sidney Rodrigues, Magaly Oliveira

{"title":"Downscaling Statistical Model Techniques for Climate Change Analysis Applied to the Amazon Region","authors":"D. Mendes, J. Marengo, Sidney Rodrigues, Magaly Oliveira","doi":"10.1155/2014/595462","DOIUrl":"https://doi.org/10.1155/2014/595462","url":null,"abstract":"The Amazon is an area covered predominantly by dense tropical rainforest with relatively small inclusions of several other types of vegetation. In the last decades, scientific research has suggested a strong link between the health of the Amazon and the integrity of the global climate: tropical forests and woodlands (e.g., savannas) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climates. Consider the importance of the Amazon biome to the global climate changes impacts and the role of the protected area in the conservation of biodiversity and state-of-art of downscaling model techniques based on ANN Calibrate and run a downscaling model technique based on the Artificial Neural Network (ANN) that is applied to the Amazon region in order to obtain regional and local climate predicted data (e.g., precipitation). Considering the importance of the Amazon biome to the global climate changes impacts and the state-of-art of downscaling techniques for climate models, the shower of this work is presented as follows: the use of ANNs good similarity with the observation in the cities of Belem and Manaus, with correlations of approximately 88.9% and 91.3%, respectively, and spatial distribution, especially in the correction process, representing a good fit.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"21 1","pages":"595462:1-595462:10"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85293458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Exponential Stability of Periodic Solution to Wilson-Cowan Networks with Time-Varying Delays on Time Scales 时间尺度上具有时变时滞的Wilson-Cowan网络周期解的指数稳定性

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/750532

Jinxiang Cai, Zhenkun Huang, Honghua Bin

引用次数: 0

Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks 利用遗传进化人工神经网络模拟现拌混凝土坍落度

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/629137

Vinay Chandwani, Vinay Agrawal, R. Nagar

{"title":"Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks","authors":"Vinay Chandwani, Vinay Agrawal, R. Nagar","doi":"10.1155/2014/629137","DOIUrl":"https://doi.org/10.1155/2014/629137","url":null,"abstract":"Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated.The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"13 1","pages":"629137:1-629137:9"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86231700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory ARTgrid:一种基于自适应共振理论的两级学习架构

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/185492

M. Švaco, B. Jerbic, F. Šuligoj

引用次数: 6

A Hybrid Intelligent Method of Predicting Stock Returns 一种预测股票收益的混合智能方法

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/246487

A. M. Rather

引用次数: 9

An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination 传输线人工神经网络保护综述:故障检测、故障分类、故障定位和故障方向识别

Adv. Artif. Neural Syst. Pub Date : 2014-01-01 DOI: 10.1155/2014/230382

Anamika Yadav, Yajnaseni Dash

{"title":"An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination","authors":"Anamika Yadav, Yajnaseni Dash","doi":"10.1155/2014/230382","DOIUrl":"https://doi.org/10.1155/2014/230382","url":null,"abstract":"Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.","PeriodicalId":7288,"journal":{"name":"Adv. Artif. Neural Syst.","volume":"526 1","pages":"230382:1-230382:20"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75059061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 80