(4) Derive the activation functions for hidden layer and output layer from the mean value function. Step 2 Calculate the Input value and Output value for hidden and output neurons using activation functions.
Here, the activation functions of the ANN are developed according to the mean value function of the selected SRGM and testing effort function .
Then, [w.sub.3] and [w.sub.4] values are estimated for mean value function [mathematical expression not reproducible] using software failure data pair ([w.sub.n], [y.sub.n]) and here [w.sub.n] is the estimated values of W(t).
(ii) The mean value function for G2 errors satisfies the following differential equation:
(iii) Similarly, the mean value function for G3 errors satisfies the following differential equation:
After this, we formulate the overall mean value function m(t) that consists of mean value functions for three generations of errors.
Then, based on the aforesaid mean value function, the failure intensity function for the proposed model, with testing-effort, can be explicitly given as
Besides, the mean value function m(t) is a concave function with respect to t.
Substituting the estimated parameters in the mean value function yields the estimation of the number of failures m([t.sub.q]) by [t.sub.q].
When the failure process is modeled by a nonhomogeneous Poisson process with a mean value function, m(t), a popular cost structure [15, 25, 27, 39] is applied as