This paper gives an additional definition to multivariate truncated power T(x|x1 ,…,xn) when the knots x1 ,…,xn do not span the whole space to which x1 ,…,xn belong. By which, together with the recurrence relation,...This paper gives an additional definition to multivariate truncated power T(x|x1 ,…,xn) when the knots x1 ,…,xn do not span the whole space to which x1 ,…,xn belong. By which, together with the recurrence relation, the practical evaluation of T(x|x1,…,xn ) will be very convenient and efficient. The same discussion is also done to box splines.展开更多
In this paper, Black Box approach is presented for behavioral modeling of a non linear power amplifier with memory effects. Large signal parameters of a Motorola LDMOS power amplifier driven by a WCDMA signal were ext...In this paper, Black Box approach is presented for behavioral modeling of a non linear power amplifier with memory effects. Large signal parameters of a Motorola LDMOS power amplifier driven by a WCDMA signal were extracted while taking into considerations the power amplifier’s bandwidth. The proposed model was validated based on the simulated data. Some validation results are presented both in the time and frequency domains, using WCDMA signal.展开更多
Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron...Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.展开更多
文摘This paper gives an additional definition to multivariate truncated power T(x|x1 ,…,xn) when the knots x1 ,…,xn do not span the whole space to which x1 ,…,xn belong. By which, together with the recurrence relation, the practical evaluation of T(x|x1,…,xn ) will be very convenient and efficient. The same discussion is also done to box splines.
文摘In this paper, Black Box approach is presented for behavioral modeling of a non linear power amplifier with memory effects. Large signal parameters of a Motorola LDMOS power amplifier driven by a WCDMA signal were extracted while taking into considerations the power amplifier’s bandwidth. The proposed model was validated based on the simulated data. Some validation results are presented both in the time and frequency domains, using WCDMA signal.
文摘Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.
