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In this case, more hidden neurons and time delay buffers maybe needed to guarantee the accuracy of the proposed general Neuro-SM model.
The verification time of BA-ELM is longer than that of BA-NN as the individual ELMs have more hidden neurons than the individual networks in BA-NN.
The reason for ELM having more hidden neurons is due to the random nature of hidden layer weights in ELM and small number of hidden neurons would usually not be able to provide adequate function representation.
Optimization of the number of hidden neurons (2 10) of the internal layer showed that the error of the validation initially decreases with more hidden neurons, but stays about constant with for 5 or more.
This is because each network in the BA-ELM has more hidden neurons than each network in BA-NN.
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A feed forward neural network based on back-propagation is a multilayered architecture made up of one or more hidden layers (2 layers–42 neurons) placed between the input (1 layer–5 neurons) and output (1 layer-1 neuron) layers.
The success rate is greater than 97% with more than 12 hidden neurons (with the exception of the case with 27 inputs).
In all cases, using 10 hidden neurons or more layer does not improve the success rate.
Furthermore, the use of 14 hidden neurons or more has a similar recognition rate to a network that contains 8 hidden neurons.
In order to improve the expressive ability of neural networks, EPCNN utilizes random connection between neurons and more hidden layers to learn the knowledge stored within the historic time series data.
This is a feed-forward, fully-connected hierarchical network typically comprising three types of neuron layers each including one or several neurons: an input layer, one or more hidden layers and an output layer.
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