A comparison of genetic algorithms and other machine learning systems on a complex classification task from common disease research
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Evolutionary neural network modeling for forecasting the field failure data of repairable systems
Expert Systems with Applications: An International Journal
Prediction of vehicle reliability performance using artificial neural networks
Expert Systems with Applications: An International Journal
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Software reliability identification using functional networks: A comparative study
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability
Expert Systems with Applications: An International Journal
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Expert Systems with Applications: An International Journal
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
Expert Systems with Applications: An International Journal
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In this study, a neural network-based model for forecasting reliability was developed. A genetic algorithm was applied for selecting neural network parameters like learning rate (@h) and momentum (@m). The input variables of the neural network model were selected by maximizing the mean entropy value. The developed model was validated by applying two benchmark data sets. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted on a load-haul-dump (LHD) machine operated at a coal mine in Alaska, USA. Past time-to-failure data for the LHD machine were collected, and cumulative time-to-failure was calculated for reliability modeling. The results demonstrate that the developed model performs well with high accuracy (R^2=0.94) in the failure prediction of a LHD machine.