A Construction Method of Differential Wavelet Neural Network Software Fault Prediction Technology

Abstract

The invention relates to a construction method of a differential wavelet neural network-based software failure prediction technology. The method comprises the following steps: 1, screening valid failure cases; 2, calculating interval time between failures, and establishing an original chaotic interval time sequence; 3, constructing training and predicting samples; 4, carrying out differential pretreatment on the training and predicting samples, and establishing a training difference time sequence and a prediction difference time sequence; 5, constructing an input matrix and an expected output matrix of a training network by means of phase space reconstruction; 6, establishing a differential wavelet neural network-based failure prediction algorithm; 7, completing training for a differential wavelet neural network, and constructing a failure prediction system; 8, inputting the prediction difference time sequence, and predicting latest failure difference time; 9, using a differential reduction method to convert an output difference predicted value into a latest predicted value of the interval time between failures of software; 10, adding the predicted difference time into the prediction difference time sequence, and applying an iteration method to realize multistep failure prediction. The construction method provided by the invention has a practical application value.

Description

technical field [0001] The invention provides a method for constructing a differential wavelet neural network software fault prediction technology, which relates to the realization of a differential wavelet neural network software fault prediction technology, and belongs to the fields of software reliability and software fault prediction. Background technique [0002] A neural network is an extensively parallel interconnected network of adaptive simple units organized to simulate the interaction of biological nervous systems with real-world objects. It has the function of large-scale parallel processing and distributed storage of various image information, and has strong fault tolerance, association and memory capabilities, so it is widely used in fault diagnosis, fault prediction, pattern recognition, associative memory, complex optimization, Image processing and computer fields. Wavelet neural network is a layered, multi-resolution new artificial neural network constructe...

AI Technical Summary

Problems solved by technology

The direct use of existing methods such as ARIMA linear prediction method and neural network nonlinear prediction method can no longer accurately predict the existing fault occurrence time

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