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A system is modelled using a multilayer perceptron network.
Bayat, F. M. et al. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits.
The result is that the capacity of a fully connected perceptron network scales strictly linear in the number of weights.
Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN).
Additionally, a multilayer perceptron network (MLP) and M5 model tree were fitted to the experimental data for comparison purposes.
Two types of ANN architectures, namely multi-layer perceptron network (MLP) and a radial basis function network (RBF) were implemented.
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In both cases, Multilayer Feedforward Perceptron networks were used.
The systems are modelled using a multilayer perceptron networks.
The analytic optimisation of the proposed method is based on multilayer perceptron networks.
This paper presents a new constructive algorithm to design multilayer perceptron networks used as classifiers.
A comparison is also made between the results of radial basis function and multi-layer perceptron networks, as well.
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