Sentence examples for I in layer from inspiring English sources

The output of neuron i in Layer 3 is obtained as y_{i}^{(3)} = mathop prod limits_{j = 1}^{k} x_{ji}^{(3)}, (1 where x ji (3) is the input from neuron j located in layer 2 to neuron i in layer 3; k is the number of fuzzy set neurons; and y i ( 3) is the output of rule neuron i in layer 3 (i.e., firing strength).

Where ( {upzeta}_{mathrm{i}}^{mathrm{n}} ) is input to neuron i in layer n, ( {mathrm{W}}_{mathrm{ji}}^{mathrm{n}} ) is the tap weight from the neuron i in layer n to neuron j in layer (n-1), ( {mathrm{O}}_{mathrm{j}}^{mathrm{n}-1} ) is the output of neuron j in layer (n-1), and m is the total number of neurons in the layer (n-1).

Therefore, the output of neuron i in Layer 3 is determined as: y_{i}^{(3)} = mathop prod limits_{j = 1}^{k} x_{ji}^{(3)} y_{prod 1}^{(3)} = xi_{A1} times xi_{B1} = xi_{1} (4 where the (xi_{I}) value gives the truth value, the firing strength, of the first rule.

For a classical random walk, the transition probability of moving from node-layer (i alpha )) to node-layer (j alpha )), i.e., within the same layer α, or to switch to the counterpart of vertex i in layer β, i.e., to node-layer (i(beta )), is uniformly distributed.

The induced local field ( {v}_j^{(l)} ) for neuron j in layer l of the BP network is   {v}_j^{(l)}={displaystyle sum_i{omega}_{ji}^{(l)}{y}_i^{left l-1right)}_i^{left l-1right_i^{left(l-1right)} ) is the output signal of the neuron i in the previous layer l-1 of the BP network and ( {omega}_{ji}^{(l)} ) is the synaptic weight of neuron j in layer l that is fed from neuron i in layer l-12 where

Besides, the area distribution of Nurr1 mRNA was different from that of the Nissl-GLI in layer 6, although they seemed to show the same borders.

end{aligned} (2) Here, a node-layer (i alpha)) means that node i participates in layer α.

Layer-4 (Consequent nodes): Every node i in this layer is an adaptive node with a node function (Eq. 8).

In particular, RORbeta and ER81 patterns were quite similar to those of Nissl-GLI in layers 4 and 5, respectively.

In AId2, layer II is broad, the cells are not densely packed and some cells extend into layer I; in LO, layer II is narrow and its cells form clusters (Figs.  3, 8).

KiM1P+ mononuclear cells of both macrophage and microglia phenotype were counted in SCD in layers I/II and in layer III and given as cells/mm.