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A new general 2-step method is proposed for more accurate determination of the network parameters.
In our system model, sensing range D is a critical parameter in the determination of the network connectivity.
The first is modelled through a macro perspective of time where the determination of the network structure and flows is accomplished.
This model can tackle the common problem encountered by conventional fuzzy back propagation neural network in the determination of the network structure in the number of hidden layers and the number of hidden neurons in each layer.
A sequential learning neural network model proposed by Zhang and Morris [1] is used in this paper to tackle the common problem encountered by conventional feed forward neural networks in the determination of the network structure in the number of hidden layers and the number of hidden neurons in each layer.
In cases where direct determination of the network is difficult or impossible it is necessary to use indirect evidence which can be more easily obtained.
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This implies a difficult but promising issue, as it concerns the determination of the network-level response to different inhibitor treatments.
An algorithm for the selection of the spread constant is applied and tests are performed for the determination of the neural network with the best performance.
Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN.
During quantitative assessment of CPR we used a diameter limitation for determination of the microvascular network.
However, when building kinetic models, the determination of the underlying network structure is just the first step [ 5].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com