Software failure time forecasting method based on kernel partial least squares regression algorithm
A kernel partial least squares, failure time technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as poor model applicability and model prediction accuracy differences, and achieve the effect of improving adaptability and realizing self-adaptive prediction.
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[0027] 1) Data normalization
[0028] When using the regression estimation algorithm to learn and predict, you first need to normalize all input and output data to the interval [0.1, 0.9], the specific conversion formula is: Among them, y is the normalized value, x is the actual value, and x max Is the maximum value in the data set, x min Is the minimum value, Δ=x max -x min , After the forecast is over, use the following mapping to map the data back to the actual value: x = y - 0.9 0.8 X Δ + x max .
[0029] 2) Problem conversion
[0030] In the software reliability prediction model based on the kernel function theory, the relationship between the software failure time data and the m failure time data that occurred before it is modeled, and the single-step prediction problem can be transformed into: Known km Observation (T 1 ,t m+1 ),(T 2 ,t m+2 ),L,(T k-m ,t k ) And the k-m+1th input T k-m+1 In the case of estimating the k-m+1th output valu...
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