Synergy of clustering multiple back propagation networks
Advances in neural information processing systems 2
Universal approximation using radial-basis-function networks
Neural Computation
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Introduction to artificial neural systems
Introduction to artificial neural systems
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Neural network design
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neurocomputing based Canadian weather analysis
Second international workshop on Intelligent systems design and application
An ensemble of neural networks for weather forecasting
Neural Computing and Applications
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Improved supply chain management based on hybrid demand forecasts
Applied Soft Computing
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Automatic extraction and identification of chart patterns towards financial forecast
Applied Soft Computing
Regularization in the selection of radial basis function centers
Neural Computation
Integrating Ensemble of Intelligent Systems for Modeling Stock Indices
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Electric load forecasting using a fuzzy ART&ARTMAP neural network
Applied Soft Computing
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
Design of ensemble neural network using entropy theory
Advances in Engineering Software
| Hi-index | 0.00 |
This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year's data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg-Marquardt algorithm. Radial basis function network (RBFN) was employed as an alternative to examine its applicability for weather forecasting. To ensure the effectiveness of neurocomputing techniques, the connectionist models were trained and tested using different datasets. Moreover, ensembles of the neural networks were generated by combining the MLPN, ERNN and RBFN using arithmetic mean and weighted average methods. Subsequently, performance of the connectionist models and their ensembles were compared with a well-established statistical technique. Experimental results obtained have shown RBFN produced the most accurate forecast model compared to ERNN and MLPN. Overall, the proposed ensemble approach produced the most accurate forecast, while the statistical model was relatively less accurate for the weather forecasting problem considered.