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雷达卡
摘要翻译：
在文献中，经常会发现需要一种方法将有限的信息集编码成单个数据的问题；通常是用手段。方法的一个重要的概括是所谓的聚合函数，还有一个值得注意的子类叫做OWA函数。然而，还有一些能够提供这种编纂的功能，但它们并不满足聚合功能的定义；预聚合和混合功能就是这种情况。本文研究了两类特殊的函数：广义混合函数和有界广义混合函数。它们同时概括了：OWA和混合函数。广义混合函数和有界广义混合函数都是以权向量是依赖于输入向量的变量的方式发展的。给出了一个特殊的广义混合算子H，并将其应用于一个简单的玩具算例。
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英文标题：
《Bounded Generalized Mixture Functions》
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作者：
Antonio Diego S. Farias, Valdigleis S. Costa, Luiz Ranyer de Ara\'ujo
Lopes, Benjam\'in Bedregal, Regivan H. N. Santiago
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
In literature, it is common to find problems which require a way to encode a finite set of information into a single data; usually means are used for that. An important generalization of means are the so called Aggregation Functions, with a noteworthy subclass called OWA functions. There are, however, further functions which are able to provide such codification which do not satisfy the definition of aggregation functions; this is the case of pre-aggregation and mixture functions. In this paper we investigate two special types of functions: Generalized Mixture and Bounded Generalized Mixture functions. They generalize both: OWA and Mixture functions. Both Generalized and Bounded Generalized Mixture functions are developed in such way that the weight vectors are variables depending on the input vector. A special generalized mixture operator, H, is provided and applied in a simple toy example.
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PDF链接：
https://arxiv.org/pdf/1806.04711
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