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The proposed approaches are applicable to chemical engineering systems exhibiting input sensitivity.
A novel sensitivity compensating nonlinear control (SCNC) approach is proposed within generic model control (GMC) framework for processes exhibiting input sensitivity.
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During use with cultured neuronal networks, the system typically exhibits input referred noise levels of only 4 6 μVRMS, such that extracellular potentials exceeding 40 μV can be readily resolved.
Another implication of this result is that, for interconnected networks to exhibit input fidelity (wherein pathways respond preferentially to authentic inputs), the basic network architecture must be embellished with an insulating mechanism(s).
The system exhibits input-referred noise levels of 5 10 μVRMS, such that extracellular potentials exceeding 50 60 μV can be readily resolved.
Fig. 1 ANN diagram exhibiting the input layer with 24 nodes, hidden layer with 2 nodes, and output layer.
The architecture of network is exhibited as l, m, n where l neurons are exhibited at input layer (equal to the number of inputs in the network), m neurons are exhibited at the hidden layer (optimized through experimentation) and n neurons are exhibited at the output layer dependent onto number of outputs preferred from model (Rene et al. 2009; Giri et al. 2011).
Young neurons exhibit high input resistance and increased susceptibility for the induction of long-term potentiation (LTP) of entorhinal glutamatergic inputs [23] [25].
The dendritic arbor is short and simple when neurons exhibit GABAergic inputs only, but has increased significantly in complexity by the time glutamatergic synapses have formed onto the neurons (Tyzio et al., 1999).
The method is validated on the problem of controlling a continuous exothermic reactor exhibiting state and input multiplicity.
In contrast, a compatible supertree is allowed to exhibit more fine-grained relationships among certain labels than those exhibited by an input tree.
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