Performance of neural networks in managerial forecasting
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
The nature of statistical learning theory
The nature of statistical learning theory
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
On the neural network approach in software reliability modeling
Journal of Systems and Software
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems with Applications: An International Journal
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Support Vector Regression (SVR), which employs the structural risk minimisation principle to minimise an upper bound of the generalisation errors instead of minimising the training errors used by ANNs, has been successfully applied to solve nonlinear forecasting and times series problems. However, the application of SVR to reliability forecasting has still not been extensively explored. In general, Recurrent Neural Networks (RNNs) are trained by back-propagation algorithms. In the study, the learning algorithms of SVR are applied to RNNs for forecasting system reliability, and the Immune Algorithm (IA) is applied to the parameter determining the SVR model. A numerical example in the existing literature is employed to demonstrate the prediction performance of the proposed model. Empirical results illustrate that the proposed model outperforms other approaches in the existing literature.