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
Neural network design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neurocomputing based Canadian weather analysis
Second international workshop on Intelligent systems design and application
Regularization in the selection of radial basis function centers
Neural Computation
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada
Engineering Applications of Artificial Intelligence
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This paper presents a comparative study of different neural network models for forecasting the weather of Vancouver, British Columbia, Canada. For developing the models, we used one years data comprising of daily maximum and minimum temperature, and wind-speed. We used Multi-Layered Perceptron (MLP) and an Elman Recurrent Neural Network (ERNN), which were trained using the one-step-secant and Levenberg-Marquardt algorithms. To ensure the effectiveness of neurocomputing techniques, we also tested the different connectionist models using a different training and test data set. Our goal is to develop an accurate and reliable predictive model for weather analysis. Radial Basis Function Network (RBFN) exhibits a good universal approximation capability and high learning convergence rate of weights in the hidden and output layers. Experimental results obtained have shown RBFN produced the most accurate forecast model as compared to ERNN and MLP networks.