Software Reliability prediction using Ensemble Model

Software Reliability is the key factor of software quality estimation and prediction during testing period. We have implemented three models such as Radial Basis Function Neural Network (RBFNN) model, Ensemble model based on two types Feed Forward Neural Networks and one Radial Basis Function Neural Network and Radial basis function Neural Network Ensembles (RNNE) model for Software reliability prediction over five benchmark datasets. We have used Bayesian regularization method on all three models to avoid over-fitting problem and generalization of the neural network. We have been used two types of meaningful performance measures such as Relative Error (RE) and Average Errors (AE) for software reliability prediction. The results of all three proposed models have been compared with some traditional models such as Duane model and Artificial neural networks like Feed Forward Neural Network (FFNN) model. The experimental result shows that the nonparametric growth model called Ensemble model (multiple predictors) shows best minimal error than parametric model. Finally, It has been observed that the multiple predictors like Ensemble model always shows the best performance than single predictor like artificial neural network and some other traditional neural networ

Investigating Predictive Capabilities of RBFNN, MLPNN and GRNN Models for LTE Cellular Network Radio Signal Power Datasets

Efficient radio frequency signal coverage planning with well configured transmitters and receivers’ communication channels, is the heart of any cost-effective cellular network design, deployment and operation. It ensures that both network quality and coverage are simultaneously make best use of (i.e. maximized). This work aim to appraise the adaptive learning and predictive capacity of three neural network models on spatial radio signal power datasets obtained from commercial LTE cellular networks. The neural network models are radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN) trained with Bayesian regulation algorithms and general regression neural network (GRNN) models.  Largely, it is established from the results that ANN prediction methods can tolerate and adapt to measurement errors of attenuating LTE radio signals. Performance comparisons reveal that all the neural network models can predict the propagated LTE radio signals with considerable errors. Specifically, RBFNN delivered the overall best performance with the smallest mean absolute percentage error, root mean square error, mean absolute error and standard deviation values. The GRNN model also gave better prediction results with marginal errors compared to the MLPNN. Thus, the predictive abilities of RBFNN and GRNN models can be explored as a useful tool to successfully plan or fine-tune mobile radio signal coverage area. Keywords: Neural networks; Signal power; attenuating radio signals; radial basis function multilayer perceptron, general regression neural network, Adaptive signal predictio

Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems

A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the system’s channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark

Predicting Fraud in Mobile Phone Usage Using Artificial Neural Networks

Mobile phone usage involves the use of wireless communication devices that can be carried anywhere, as they require no physical connection to any external wires to work. However, mobile technology is not without its own problems. Fraud is prevalent in both fixed and mobile networks of all technologies. Frauds have plagued the telecommunication industries, financial institutions and other organizations for a long time. The aim of this research work and research publication is to apply 3 different neural network models (Fuzzy, Radial Basis and the Feedforward) to the prediction of fraud in real-life data of phone usage and also analyze and evaluate their performances with respect to their predicting capability. From the analysis and model predictability experiment carried out in this scientific research work, it was discovered that the fuzzy network model had the minimum error generated in its fraud predicting capability. Thus, its performance in terms of the error generated in this fraud prediction experiment showed that its NMSE (Normalized mean squared error) for the fraud predicted was 1.98264609. The mean absolute error (M AE = 15.00987244) for its fraud prediction was also the least; this showed that the fuzzy model fraud predictability was much better than the other two models

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Forecasting the geomagnetic activity of the Dst Index using radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited input-output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. Radial basis function (RBF) networks are an important and popular network model for nonlinear system identification and dynamical modelling. A novel generalised multiscale RBF (MSRBF) network is introduced for Dst index modelling. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using an orthogonal least squares (OLS) type algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems