LEADER 04015nam 2200625Ia 450 001 9910437919903321 005 20200520144314.0 010 $a9783642294914 010 $a364229491X 024 7 $a10.1007/978-3-642-29491-4 035 $a(CKB)3390000000030173 035 $a(SSID)ssj0000746146 035 $a(PQKBManifestationID)11378867 035 $a(PQKBTitleCode)TC0000746146 035 $a(PQKBWorkID)10878137 035 $a(PQKB)11189026 035 $a(DE-He213)978-3-642-29491-4 035 $a(MiAaPQ)EBC3070935 035 $a(PPN)168314673 035 $a(EXLCZ)993390000000030173 100 $a20120324d2013 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aSupervised learning with complex-valued neural networks /$fSundaram Suresh, Narasimhan Sundararajan, and Ramasamy Savitha 205 $a1st ed. 2013. 210 $aHeidelberg ;$aNew York $cSpringer$dc2013 215 $a1 online resource (XXII, 170 p.) 225 1 $aStudies in computational intelligence,$x1860-949X ;$v421 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783642426797 311 08$a3642426794 311 08$a9783642294907 311 08$a3642294901 320 $aIncludes bibliographical references. 327 $aIntroduction -- Fully Complex-valued Multi Layer Perceptron Networks -- Fully Complex-valued Radial Basis Function Networks -- Performance Study on Complex-valued Function Approximation Problems -- Circular Complex-valued Extreme Learning Machine Classifier -- Performance Study on Real-valued Classification Problems -- Complex-valued Self-regulatory Resource Allocation Network -- Conclusions and Scope for FutureWorks (CSRAN). 330 $aRecent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems. 410 0$aStudies in computational intelligence ;$vv. 421. 606 $aSupervised learning (Machine learning) 606 $aNeural networks (Computer science) 615 0$aSupervised learning (Machine learning) 615 0$aNeural networks (Computer science) 676 $a006.31 700 $aSuresh$b Sundaram$01061257 701 $aSundararajan$b Narasimhan$01755245 701 $aSavitha$b Ramasamy$01755246 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437919903321 996 $aSupervised learning with complex-valued neural networks$94191966 997 $aUNINA