The GC can be modeled as Eq. (13); in the proposed ISP, the GC is implemented using a polynomial approximation: Ihboxleft x,yright)= AI{left x,yright)}^{gamma } (13 where I ' is output image, I is input image, and γ is gamma value.
Thus, this is a two-stage model where K are system parameters, k i are output linkage parameters, and k (j) = k(j) + εj where <εj> = 0 and the variance σ(εj) depends on the fitted value k(j); εj is measurement noise and <εj> is its mean.
Efficiency can be define mathematically as E = 1 - frac{{mathop sum nolimits_{i = 1}^{N} (T_{i} - O_{i} )^{2} }}{{sumnolimits_{i = 1}^{N} {left( {T_{i} - {bar{T}}_{i}} right)^{2} } }}where T i is measured output (Target), O i is the predicted output from ANN and ({bar{T}}_{i}) is the mean measured output.
The natural logarithm of the experimentally determined EC50 value of each compound i was used as output for the ANN models (exp i = ln EC50, i ).
The basic formula for determining the RMSE is: {text{RMSE}} = sqrt {frac{{(O_{i} - T_{i} )^{2} }}{N}}where, T i is measured output (Target), O i is the predicted output and N represent the number of input output data pairs.
The measured output is given by (25) where h i is the output function.
K Ti is the voltage ratio of transformer; V Ti and i Ti are the output voltage and current at the high-voltage side of transformer T i, respectively; V i and i i are the output voltage and current at the low-voltage side of transformer T i, respectively; X Li is the equivalent impedance of Line i.
where is the weight connecting node to node and out i is the output from node.
Y i is the output target value for the ith training vector.
where w i is the output of f(x i ) and k is in the range [0,M].
where (tilde {p_{i}}) is the predicted probability, x i is the output of the fully connected layer, and p i is the true label.
