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<dc:title xml:lang="pl"><![CDATA[Neural networks in the framework of granular computing]]></dc:title>
<dc:creator><![CDATA[Pedrycz, Witold (1953- )]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[information granulation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[pyramid architectures]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[encoding and decoding]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[knowledge representation]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The study is concerned with the fundamentals of granular computing and its application to neural networks. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (embracing a number of individual entitites) and their ensuing processing. We elaborate on the rationale behind granular computing.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Next, a number of formal frameworks of information granulation are discussed including several alternatives such as fuzzy sets, interval analysis, rough sets, and probability. The notion of granularity itself is defined and quantified. A design agenda of granular computing is formulated and the key design problems are raised. A number of granular architectures are also discussed with an objective of dealineating the fundamental algorithmic and conceptual challenges.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[It is shown that the use of information granules of different size (granularity) lends itself to general pyramid architectures of information processing. The role of encoding and decoding mechanisms visible in this setting is also discussed in detail along with some particular solutions. Neural networks are primarily involved at the level of numeric optimization.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Granularity of information introduces another dimension to the neurocomputing. We discuss the role of granular constructs in the design of neural networks and knowledge representation therein. The intent of this paper is to elaborate on the fundamentals and put the entire area in a certain perspective while not moving into specific algorithmic details.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Rutkowska, Danuta - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Zadeh, Lotfi A. - ed.]]></dc:contributor>
<dc:date><![CDATA[2000]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/58745/AMCS_2000_10_4_4.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/65524/edition/58745/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:58745]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 10, number 4 (2000)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=paper&paper=925]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:65524]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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