Applied Artificial Neural Networks

Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves-in terms of training, topologies, types, etc.-a similar a...

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Bibliographic Details
Main Author: Christian Dawson (Ed.) (auth)
Format: Book Chapter
Published: MDPI - Multidisciplinary Digital Publishing Institute 2016
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Online Access:Get Fullteks
DOAB: description of the publication
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020 |a books978-3-03842-271-6 
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042 |a dc 
100 1 |a Christian Dawson (Ed.)  |4 auth 
245 1 0 |a Applied Artificial Neural Networks 
260 |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2016 
300 |a 1 electronic resource (XIV, 244 p.) 
506 0 |a Open Access  |2 star  |f Unrestricted online access 
520 |a Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves-in terms of training, topologies, types, etc.-a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc. This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine. 
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546 |a English 
653 |a Deep learning 
653 |a Artificial Neural Networks 
653 |a Data Mining 
653 |a Machine Learning 
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