Modular Network for Generalized Self-Organizing Map
نویسندگان
چکیده
منابع مشابه
The Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملAutomated Network Drawing Using Self-Organizing Map
In this paper, a method for automatically creating circuit schematic diagrams from the topological information contained in network data files has been proposed. This method is based on Self-Organizing Map (SOM) neural network and the basic idea behind the method is to let the network span itself according to a given “shape” of the network grid. The topology of a network is defined by the conne...
متن کاملA Modfied Self-organizing Map Neural Network to Recognize Multi-font Printed Persian Numerals (RESEARCH NOTE)
This paper proposes a new method to distinguish the printed digits, regardless of font and size, using neural networks.Unlike our proposed method, existing neural network based techniques are only able to recognize the trained fonts. These methods need a large database containing digits in various fonts. New fonts are often introduced to the public, which may not be truly recognized by the Opti...
متن کاملImproving Self Organizing Map Performance for Network Intrusion Detection
The continuous evolution of the types of attacks against computer networks suggests a paradigmatic shift from misuse based intrusion detection system to anomaly based systems. Unsupervised learning algorithms are natural candidates for this task, but while they have been successfully applied in host-based intrusion detection, network-based applications are more difficult, for a variety of reaso...
متن کاملLandforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE ESS Fundamentals Review
سال: 2020
ISSN: 1882-0875
DOI: 10.1587/essfr.14.2_97