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Artificial Neural Networks in Hydrology (Paperback, Softcover reprint of hardcover 1st ed. 2000)
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Artificial Neural Networks in Hydrology (Paperback, Softcover reprint of hardcover 1st ed. 2000)
Series: Water Science and Technology Library, 36
Expected to ship within 10 - 15 working days
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R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering
Purdue University West Lafayette, IN. , USA Background and
Motivation The basic notion of artificial neural networks (ANNs),
as we understand them today, was perhaps first formalized by
McCulloch and Pitts (1943) in their model of an artificial neuron.
Research in this field remained somewhat dormant in the early
years, perhaps because of the limited capabilities of this method
and because there was no clear indication of its potential uses.
However, interest in this area picked up momentum in a dramatic
fashion with the works of Hopfield (1982) and Rumelhart et al.
(1986). Not only did these studies place artificial neural networks
on a firmer mathematical footing, but also opened the dOOf to a
host of potential applications for this computational tool.
Consequently, neural network computing has progressed rapidly along
all fronts: theoretical development of different learning
algorithms, computing capabilities, and applications to diverse
areas from neurophysiology to the stock market. . Initial studies
on artificial neural networks were prompted by adesire to have
computers mimic human learning. As a result, the jargon associated
with the technical literature on this subject is replete with
expressions such as excitation and inhibition of neurons, strength
of synaptic connections, learning rates, training, and network
experience. ANNs have also been referred to as neurocomputers by
people who want to preserve this analogy.
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