03932nam 2200589Ia 450 991043791990332120200520144314.03-642-29491-X10.1007/978-3-642-29491-4(CKB)3390000000030173(SSID)ssj0000746146(PQKBManifestationID)11378867(PQKBTitleCode)TC0000746146(PQKBWorkID)10878137(PQKB)11189026(DE-He213)978-3-642-29491-4(MiAaPQ)EBC3070935(PPN)168314673(EXLCZ)99339000000003017320120324d2013 uy 0engurnn|008mamaatxtccrSupervised learning with complex-valued neural networks /Sundaram Suresh, Narasimhan Sundararajan, and Ramasamy Savitha1st ed. 2013.Heidelberg ;New York Springerc20131 online resource (XXII, 170 p.) Studies in computational intelligence,1860-949X ;421Bibliographic Level Mode of Issuance: Monograph3-642-42679-4 3-642-29490-1 Includes bibliographical references.Introduction -- 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).Recent 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.Studies in computational intelligence ;v. 421.Supervised learning (Machine learning)Neural networks (Computer science)Supervised learning (Machine learning)Neural networks (Computer science)006.31Suresh Sundaram1061257Sundararajan Narasimhan1755245Savitha Ramasamy1755246MiAaPQMiAaPQMiAaPQBOOK9910437919903321Supervised learning with complex-valued neural networks4191966UNINA