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  • In neural network design, the engineer or designer chooses the network topology, the trigger function or performance function, learning rule and the criteria for stopping the training phase. So, it is pretty difficult determining the size and parameters of the network as there is no rule or formula to do it. The best we can do for having success with our design is playing with it. The problem with this method is when the system does not work properly it is hard to refine the solution. Despite this issue, neural networks based solution is very efficient in terms of development, time and resources. Artificial neural networks provide real solutions that are difficult to match with other technologies.
  • In neural network design, the engineer or designer chooses the network topology, the trigger function or performance function, learning rule and the criteria for stopping the training phase. So, it is pretty difficult determining the size and parameters of the network as there is no rule or formula to do it. The best we can do for having success with our design is playing with it. The problem with this method is when the system does not work properly it is hard to refine the solution. Despite this issue, neural networks based solution is very efficient in terms of development, time and resources. Artificial neural networks provide real solutions that are difficult to match with other technologies. (en)
Title
  • Application of Neural Networks in Teaching
  • Application of Neural Networks in Teaching (en)
skos:prefLabel
  • Application of Neural Networks in Teaching
  • Application of Neural Networks in Teaching (en)
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  • RIV/63468352:_____/12:#0000177!RIV12-MSM-63468352
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  • RIV/63468352:_____/12:#0000177
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  • neural networks, artificial neural network, structured processing data, neural network simulators (en)
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  • Application of Neural Networks in Teaching
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  • Jurča, Robert
  • Petrucha, Jindřich
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  • DTI
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  • 978-80-89400-26-3
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