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  • Model
  • The very nature of this particular model is that it will force the output to one of nearby values if a variation of input is fed to the network that it is not trained for, thus solving the proximity issue. (codeproject.com)
  • Results show that overall the random forest model performs better than the multilayer perceptron in terms of root mean squared error and mean absolute error. (diva-portal.org)
  • estimation
  • The simulation results indicate a decrease in estimation error values that depicts its ability to enhance the function approximation capability and consequently exhibits excellent learning ability compared to the conventional neural network with sigmoid or other activation functions. (springer.com)
  • topology
  • We consider dynamic configuration of wireless sensor networks, where certain functions can be automatically assigned to nodes at any time of network operations, based on the parameters such as remaining energy and topology changes. (archive.org)
  • images
  • Towards Automatic Registration of Magnetic Resonance Images of the Brain Using Neural Networks. (psu.edu)
  • The technical approach followed in processing input images, detecting graphic symbols, analyzing and mapping the symbols and training the network for a set of desired Unicode characters corresponding to the input images are discussed in the subsequent sections. (codeproject.com)
  • optimization
  • This method will be in futur works applied to logical rule extraction and architecture optimization in those architectures but also in recurrent networks. (psu.edu)
  • values
  • The multilayer perceptron is performed using both the original response cloud top pressure and a log transformed repsonse to avoid negative values as output which is prevalent when using the original response. (diva-portal.org)