Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Expert Systems with Applications: An International Journal
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Analysis and modeling of multivariate chaotic time series based on neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimization of system reliability in multi-factory production networks by maintenance approach
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
Expert Systems with Applications: An International Journal
A grey approach for forecasting in a supply chain during intermittentdisruptions
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
An accurate product reliability prediction model can not only learn and track the product's reliability and operational performance, but also offer useful information for managers to take follow-up actions to improve the product' quality and cost. This study proposes a new method for predicting the reliability for repairable systems. The novel method constructs a predictive model by employing evolutionary neural network modeling approach. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer and learning parameters of the neural network architecture. Moreover, two case studies are presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of other methods to illustrate the feasibility and effectiveness of the proposed method.