000 03546nam a22005775i 4500
001 978-3-540-72432-2
003 DE-He213
005 20141014113522.0
007 cr nn 008mamaa
008 100301s2007 gw | s |||| 0|eng d
020 _a9783540724322
_9978-3-540-72432-2
024 7 _a10.1007/978-3-540-72432-2
_2doi
041 _aeng
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aMelin, Patricia.
_eeditor.
245 1 0 _aAnalysis and Design of Intelligent Systems using Soft Computing Techniques
_h[electronic resource] /
_cedited by Patricia Melin, Oscar Castillo, Eduardo Gomez Ramírez, Janusz Kacprzyk, Witold Pedrycz.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _aXXI, 855 p. Also available online.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Soft Computing,
_x1615-3871 ;
_v41
505 0 _aFuzzy Logic as the Logic of Natural Languages -- I: Type-2 Fuzzy Logic: Theory and Applications -- II: Fuzzy Clustering: Theory and Applications -- III: Intelligent Identification and Control -- IV: Time Series Prediction -- V: Pattern Recognition -- VI: Evolutionary Computation -- VII: Fuzzy Modeling -- VIII: Intelligent Manufacturing and Scheduling -- IX: Intelligent Agents -- X: Neural Networks Theory -- XI: Robotics -- XII: Fuzzy Logic Applications.
520 _aThis book comprises a selection of papers from IFSA 2007 on new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, intelligent control, or other application areas.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aMathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aApplications of Mathematics.
700 1 _aCastillo, Oscar.
_eeditor.
700 1 _aRamírez, Eduardo Gomez.
_eeditor.
700 1 _aKacprzyk, Janusz.
_eeditor.
700 1 _aPedrycz, Witold.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540724315
830 0 _aAdvances in Soft Computing,
_x1615-3871 ;
_v41
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-72432-2
912 _aZDB-2-ENG
942 _cEB
999 _c3162
_d3162