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Books > Computing & IT > Applications of computing > Artificial intelligence
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Supervised Learning with Complex-valued Neural Networks (Hardcover, 2013 ed.)
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Supervised Learning with Complex-valued Neural Networks (Hardcover, 2013 ed.)
Series: Studies in Computational Intelligence, 421
Expected to ship within 10 - 15 working days
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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.
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