发布求助
文献互助 智能选刊 最新文献

Applications of Artificial Neural Networks for Nonlinear Data最新文献

筛选
英文 中文
Fault Severity Sensing for Intelligent Remote Diagnosis in Electrical Induction Machines 基于故障程度感知的感应电机智能远程诊断
Applications of Artificial Neural Networks for Nonlinear Data Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-4042-8.ch008
Saad Chakkor, Mostafa Baghouri, Abderrahmane Hajraoui
{"title":"Fault Severity Sensing for Intelligent Remote Diagnosis in Electrical Induction Machines","authors":"Saad Chakkor, Mostafa Baghouri, Abderrahmane Hajraoui","doi":"10.4018/978-1-7998-4042-8.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-4042-8.ch008","url":null,"abstract":"Electrical induction machines are widely used in the modern wind power production. As their repair cost is important and since their down-time leads to significant income loss, increasing their reliability and optimizing their proactive maintenance process are critical tasks. Many diagnosis systems have been proposed to resolve this issue. However, these systems are failing to recognize accurately the type and the severity level of detected faults in real time. In this chapter, a remote automated control approach applied for electrical induction machines has been suggested as an appropriate solution. It combines developed Fast-ESPRIT method, fault classification algorithm, and fuzzy inference system interconnected with vibration sensors, which are located on various wind turbine components. Furthermore, a new fault severity indicator has been formulated and evaluated to avoid false alarms. Study findings with computer simulation in Matlab prove the satisfactory robustness and performance of the proposed technique in fault classification and diagnosis.","PeriodicalId":198666,"journal":{"name":"Applications of Artificial Neural Networks for Nonlinear Data","volume":" 36","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113949061","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
Literature Survey for Applications of Artificial Neural Networks 人工神经网络应用文献综述
Applications of Artificial Neural Networks for Nonlinear Data Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-4042-8.CH001
P. Pancholi, Sonal Jayantilal Patel
{"title":"Literature Survey for Applications of Artificial Neural Networks","authors":"P. Pancholi, Sonal Jayantilal Patel","doi":"10.4018/978-1-7998-4042-8.CH001","DOIUrl":"https://doi.org/10.4018/978-1-7998-4042-8.CH001","url":null,"abstract":"The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.","PeriodicalId":198666,"journal":{"name":"Applications of Artificial Neural Networks for Nonlinear Data","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102175","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: 2
Applications of ANN for Agriculture Using Remote Sensed Data 人工神经网络在农业遥感数据中的应用
Applications of Artificial Neural Networks for Nonlinear Data Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-4042-8.ch004
G. M., A. Karegowda, N. Rudrappa, D. G.
{"title":"Applications of ANN for Agriculture Using Remote Sensed Data","authors":"G. M., A. Karegowda, N. Rudrappa, D. G.","doi":"10.4018/978-1-7998-4042-8.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-4042-8.ch004","url":null,"abstract":"Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defense, intelligence, commerce, economics, and administrative planning. Remote sensing is used in science and technology, and through it, an object can be identified, measured, and analyzed without physical presence for interpretation. In India remote sensing has been using since 1970s. One among these applications is the crop classification and yield estimation. Using remote sensing in agriculture for crop mapping, and yield estimation provides efficient information, which is mainly used in many government organizations and the private sector. The pivotal sector for ensuring food security is a major concern of interest in these days. In time, availability of information on agricultural crops is vital for making well-versed decisions on food security issues.","PeriodicalId":198666,"journal":{"name":"Applications of Artificial Neural Networks for Nonlinear Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130228007","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: 0
Fundamental Categories of Artificial Neural Networks 人工神经网络的基本分类
Applications of Artificial Neural Networks for Nonlinear Data Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-4042-8.CH003
Arunaben Prahladbhai Gurjar, S. Patel
{"title":"Fundamental Categories of Artificial Neural Networks","authors":"Arunaben Prahladbhai Gurjar, S. Patel","doi":"10.4018/978-1-7998-4042-8.CH003","DOIUrl":"https://doi.org/10.4018/978-1-7998-4042-8.CH003","url":null,"abstract":"The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.","PeriodicalId":198666,"journal":{"name":"Applications of Artificial Neural Networks for Nonlinear Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129805474","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: 1
Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN 神经网络的元启发式参数优化及其实时应用
Applications of Artificial Neural Networks for Nonlinear Data Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-4042-8.ch010
A. Karegowda, D. G.
{"title":"Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN","authors":"A. Karegowda, D. G.","doi":"10.4018/978-1-7998-4042-8.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-4042-8.ch010","url":null,"abstract":"Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.","PeriodicalId":198666,"journal":{"name":"Applications of Artificial Neural Networks for Nonlinear Data","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116960493","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信