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Start Over Search Limiters Available in Library Collection Topic artificial neural network Topic artificial neural networks Topic reliability (personality trait) Publication Type eBooks
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1. Software Reliability Estimation Based on Cubic Splines.

Author

Bandara, P. L. M. Kelani, Wikramanayake, G. N., and Goonethillake, J. S.

Subjects
*COMPUTER software development, *RELIABILITY (Personality trait), *ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *POISSON processes
Abstract

Software reliability is one of the most important software quality attribute and Software reliability estimation is a hard problem to solve accurately. However for management of software quality and standard practice of the organization, accurate reliability estimation is important. Non-homogeneous Poisson Process (NHPP) models and Artificial Neural Network (ANN) models are among the most important software reliability growth models. In this paper we study an approach using past fault-related data with cubic spline Network model to estimate reliability. A numerical example is shown with simulated datasets. The example shows that the proposed model accurately estimate the software reliability. [ABSTRACT FROM AUTHOR]

Published
2009

2. A new approach for solving inverse reliability problems with implicit response functions

Author

Cheng, Jin, Zhang, Jie, Cai, C.S., and Xiao, Ru-Cheng

Subjects
*ARTIFICIAL neural networks, *ARTIFICIAL intelligence, *EVOLUTIONARY computation, *RELIABILITY (Personality trait)
Abstract

Abstract: The inverse first-order reliability method (FORM) is one of the most widely used methods in inverse reliability analysis. However, this method has two drawbacks in the solution of inverse reliability problems with implicit response functions. First, it requires the evaluation of the derivatives of the response functions with respect to the random variables. When these functions are implicit functions of the random variables, derivatives of these response functions are not readily available. Second, it usually involves repeated deterministic response analyses of complicated structures due to the variation of the basic variables, and therefore requires a relatively long computation time. To overcome these drawbacks of the inverse FORM, an artificial neural network (ANN)-based inverse FORM is proposed in this paper. In this method, an ANN model is used to approximate the structural response function so that the number of deterministic response analyses can be dramatically reduced. The explicit formulation of structural response is derived by using the parameters of the ANN model. After the explicit response function is determined, the inverse FORM is applied to solve the inverse reliability problem. The accuracy and efficiency of the proposed method is demonstrated through two numerical examples. Some important parameters in the proposed method are also discussed. [Copyright &y& Elsevier]

Published
2007
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