<link rel="stylesheet" href="styles.cf390c6bfbe7555f.css">

學術出版;;Publications

Permanent URI for this communityhdl:11536/1

收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

Browse

🏛️國立陽明交通大學機構典藏

Subject: search.filters.subject.Unsupervised learning

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item type:Item,
    Affinity Propagation Clustering for Interference Management in Aerial Small Cells
    (2019-01-01)
    Cheng, Shao-Hung
    ;
    Chao, Yung-Sheng
    ;
    Wang, Li-Chun
    ;
    Tsai, Ang-Hsun
    ;
    電機工程學系
    The drone small cell (DSC) network has become a key technology for air-to-ground wireless communications in a variety of temporary or emergency situations. Based on mobile users, frequently changing DSC topologies have important challenges such as severe co-channel interference and limited battery capacity. However, temporarily dispatched drones cannot obtain labeled and historical data in advance, while they only obtain real-time operational data. The observed data can be analyzed by unsupervised learning methods to find useful information for resource management. In this paper, an interference-aware power control (IPC) framework is designed using affinity propagation clustering (APC). The APC method is one of the unsupervised learning methods. The numerical results show that our proposed IPC framework using the APC method can reduce system interference and significantly improve the energy efficiency of DSC networks.
  • Thumbnail Image
    Item type:Item,
    Self-adjusting feature maps network and its applications
    (2016-09-26)
    Li, Dong-Lin
    ;
    Prasad, Mukesh
    ;
    Lin, Chin-Teng
    ;
    Chang, Jyh-Yeong
    ;
    資訊工程學系
    This paper, proposes a novel artificial neural network, called self-adjusting feature map (SAM), and develop its unsupervised learning ability with self-adjusting mechanism. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. The SAM can automatically isolate a set of connected neurons, in which, the used number of the sets may indicate the number of clusters. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advantages and retreat of waste. In the training process,, for each representative neuron has are three phases, growth, adaptation and decline. The network of representative neurons, first create the necessary neurons according to the local density of the input data in the growth phase. In the adaption phase, it adjusts neighborhood neuron pair\'s connected/disconnected topology constantly according to the statistics of input feature data. Finally, the unnecessary neurons of the network are merged or remove in the decline phase. In this paper, we exploit the SAM to handle some peculiar cases that cannot be handled easily by classical unsupervised learning networks such as self-organizing map (SOM) network. The remarkable characteristics of the SAM can be seen on various real world cases in the experimental results. (C) 2016 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Item type:Item,
    Self-Adjusting Feature Maps Network
    (2011)
    Lin, Chin-Teng
    ;
    Li, Dong-Lin
    ;
    Chang, Jyh-Yeong
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    In this paper, we propose a novel artificial neural network, called self-adjusting feature map (SAM), and its unsupervised learning algorithm with self-adjusting mechanism. After the training of SAM network, we will obtain a map composed of a set of representative connected neurons. The trained network structure of representative connected neurons not only displays the spatial relation of the input data distribution but also quantizes the data well. SAM can automatically isolate a set of connected neurons, in which the number of the set may indicate the number of clusters to be used. The idea of self-adjusting mechanism is based on combining of mathematical statistics and neurological advance and retreat of waste. For each representative neuron, there are three periods, growth. adaptation and decline, in its training process. The network of representative neurons will first create the necessary neurons according to the local density of the input data in the growth period. Then it will adjust neighborhood neuron pair's connected/disconnected topology constantly according to the statistics of input feature data in the adaptation period. Lastly the unnecessary neurons of the network will be merged or deleted in the decline period. In this study. we exploit SAM to handle some peculiar cases that cannot be well dealt with by classical unsupervised learning networks such as self-organizing feature map (SOM) network. Furthermore, we also take several real world cases to exhibit the remarkable characteristics of SAM.