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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Hardcover, 2004 ed.)
Loot Price: R4,337
Discovery Miles 43 370
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Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications (Hardcover, 2004 ed.)
Series: Signals and Communication Technology
Expected to ship within 12 - 17 working days
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Neurofuzzy and fuzzyneural techniques as tools of studying and
analyzing complex problems are relatively new even though neural
networks and fuzzy logic systems have been applied as computational
intelligence structural e- ments for the last 40 years.
Computational intelligence as an independent sci- tific field has
grown over the years because of the development of these str- tural
elements. Neural networks have been revived since 1982 after the
seminal work of J. J. Hopfield and fuzzy sets have found a variety
of applications since the pub- cation of the work of Lotfi Zadeh
back in 1965. Artificial neural networks (ANN) have a large number
of highly interconnected processing elements that usually operate
in parallel and are configured in regular architectures. The c-
lective behavior of an ANN, like a human brain, demonstrates the
ability to learn, recall, and generalize from training patterns or
data. The performance of neural networks depends on the
computational function of the neurons in the network, the structure
and topology of the network, and the learning rule or the update
rule of the connecting weights. This concept of trainable neural n-
works further strengthens the idea of utilizing the learning
ability of neural networks to learn the fuzzy control rules, the
membership functions and other parameters of a fuzzy logic control
or decision systems, as we will explain later on, and this becomes
the advantage of using a neural based fuzzy logic system in our
analysis. On the other hand, fuzzy systems are structured numerical
est
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