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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yousif Al Mashhadany | - |
dc.date.accessioned | 2022-10-20T05:23:03Z | - |
dc.date.available | 2022-10-20T05:23:03Z | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 978-953-51-0409-4 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/3658 | - |
dc.description.abstract | During almost three decades, the study on theory and applications of artificial neural network has increased considerably, due partly to a number of significant breakthroughs in research on network types and operational characteristics, but also because of some distinct network with feedback (closed-loop) connects, have been an important focus of research and development. Examples include bidirectional associative memory (BAM), Hopfield, cellular neural network (CNN), Boltzmann machine, and recurrent back propagation nets, etc. RNN techniques have been applied to a wide variety of problems due to their dynamics and parallel distributed property, such as identifying and controlling the real-time system, neural computing, image processing and so on. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Publisher InTech | en_US |
dc.subject | Recurrent Neural Network | en_US |
dc.subject | Human Simulator | en_US |
dc.subject | Virtual Reality | en_US |
dc.title | Recurrent Neural Network with Human Simulator Based Virtual Reality | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | الهندسة الكهربائية |
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