Autonomous Learning Systems:From Data to Knowledge in Real Time
Autonomous Learning Systems:From Data to Knowledge in Real Time
复制标题
自主学习系统：从实时数据到知识
DOI：
10.4049/jimmunol.144.11.4327
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发表时间：
2012
影响因子：
4.4
通讯作者：
P. Angelov
中科院分区：
文献类型：
--
作者：
P. Angelov
自主学习系统是这个新兴领域十多年来集中研究的结果，该领域跨越了许多众所周知和成熟的学科，包括机器学习、系统识别、数据挖掘、模糊逻辑、神经网络、神经模糊系统、控制理论和模式识别。这些系统的发展既是由国防和安全、航空航天和先进加工工业、生物医学和智能交通等领域日益增长的需求驱动的行业驱动，也是由研究驱动——所有上述成熟的研究学科都存在着强烈的创新趋势，与其在线和实时应用相关；他们的适应性和灵活性。本书介绍了关键技术，对方法进行了详细的技术解释，并说明了该方法与广泛应用的实际相关性，以系统的方法解决了自主学习系统的挑战，为快速发展的研究领域奠定了基础，该领域将支撑对工业和社会至关重要的一系列技术应用。主要特点： • 系统地介绍该主题，从解释基本原理到通过大量应用说明所提出的方法。 • 涵盖无人驾驶车辆/机器人、炼油厂、化工、不断变化的用户行为和活动识别等领域的广泛应用。 • 通过不断发展和自主学习机制的棱镜回顾传统领域，包括聚类、分类、控制、故障检测和异常检测、过滤和估计 • 附带一个托管附加材料的网站，包括软件工具箱和讲义 自主学习系统为学者、学生、研究人员和执业工程师提供了有关该主题的“一站式商店”。它对于政府机构和软件开发商来说也是有价值的参考。
Autonomous Learning Systems is the result of over a decade of focused research and studies in this emerging area which spans a number of well-known and well-established disciplines that include machine learning, system identification, data mining, fuzzy logic, neural networks, neuro-fuzzy systems, control theory and pattern recognition. The evolution of these systems has been both industry-driven with an increasing demand from sectors such as defence and security, aerospace and advanced process industries, bio-medicine and intelligent transportation, as well as research-driven – there is a strong trend of innovation of all of the above well-established research disciplines that is linked to their on-line and real-time application; their adaptability and flexibility. Providing an introduction to the key technologies, detailed technical explanations of the methodology, and an illustration of the practical relevance of the approach with a wide range of applications, this book addresses the challenges of autonomous learning systems with a systematic approach that lays the foundations for a fast growing area of research that will underpin a range of technological applications vital to both industry and society. Key features: • Presents the subject systematically from explaining the fundamentals to illustrating the proposed approach with numerous applications. • Covers a wide range of applications in fields including unmanned vehicles/robotics, oil refineries, chemical industry, evolving user behaviour and activity recognition. • Reviews traditional fields including clustering, classification, control, fault detection and anomaly detection, filtering and estimation through the prism of evolving and autonomously learning mechanisms • Accompanied by a website hosting additional material, including the software toolbox and lecture notes Autonomous Learning Systems provides a ‘one-stop shop’ on the subject for academics, students, researchers and practicing engineers. It is also a valuable reference for Government agencies and software developers.