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Neurocomputing in Complex Domain

  • Bipin Kumar TripathiEmail author
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Part of the Studies in Computational Intelligence book series (SCI, volume 571)

Abstract

There are many areas of applications which involve signals that are inherently complex-valued. The characteristics of these applications can be effectively realized if they are operated with the complex-valued neural networks (CVNNs). Apart from that it is also widely observed in researches that the real-valued problems can be solved far efficiently if they are represented and operated in the complex domain. Therefore, CVNNs have emerged a very good alternative in second generation of neurocomputing. The CVNNs to preserve and process the data (signals) in the complex domain itself are gaining more attention over their real-valued counterparts. The use of neural networks is naturally accompanied by the trade-off between issues such as the overfitting, generalization capability, local minima problems, and stability of the weight-update system. The main obstacle in the development of a complex-valued neural network (CVNN) and its learning algorithm is the selection of an appropriate activation function and error function (EF). It can be said that the suitable error function-based training scheme with a proper choice of activation function can substantially decrease the epochs and improve the generalization ability for the problem in question. This chapter presents prominent functions as a basis for making these choices and designing a learning scheme. The choice of EF and activation function in the training scheme also circumvents some of the existing lacunae such as error getting stuck and not progressing below a certain value. This chapter further introduces a novel approach to improve resilient propagation in complex domain for fast learning.

Keywords

Activation Function Error Function Complex Domain Real Domain Absolute Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

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