Multilayer feedforward networks are universal approximators
Neural Networks
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Engineering Applications of Artificial Intelligence
Neural Networks for Real-Time Traffic Signal Control
IEEE Transactions on Intelligent Transportation Systems
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
IEEE Transactions on Neural Networks
Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
IEEE Transactions on Neural Networks
Prediction Interval Construction and Optimization for Adaptive Neurofuzzy Inference Systems
IEEE Transactions on Fuzzy Systems
Prediction Intervals to Account for Uncertainties in Travel Time Prediction
IEEE Transactions on Intelligent Transportation Systems
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Point forecasts suffer from unreliable and uninformative problems when the uncertainty level increases in data. Prediction intervals (PIs) have been proposed in the literature to quantify uncertainties associated with point forecasts. In this paper, a newly introduced method called Lower Upper Bound Estimation (LUBE) (Khosravi et al., 2011, [1]) is applied and extended for construction of PIs. The LUBE method adopts a neural network (NN) with two outputs to directly generate the upper and lower bounds of PIs without making any assumption about the data distribution. A new width evaluation index that is suitable for NN training is proposed. Further a new cost function is developed for the comprehensive evaluation of PIs based on their width and coverage probability. The width index is replaced by the new one and PSO with mutation operator is used for minimizing the cost function and adjusting NN parameters in the LUBE method. By introducing these two changes we observe dramatic improvements in the quality of results and speed. Demonstrated results for six synthetic and real-world case studies indicate that the proposed PSO-based LUBE method is very efficient in constructing high quality PIs in a short time.