This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.

/pdf/an-ensemble-of-neural-networks-for-weather-forecasting-fperv3qtga.pdf

An ensemble of neural networks for weather forecasting

Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada

In speaker identification, most of the computation originates from distance or likelihood computations between the feature vectors of the unknown speaker and the models in the database. The identification time depends on the number of feature vectors, their dimensionality, the complexity of the speaker models and the number of speakers. In this paper, we focus on optimizing vector quantization (VQ) based speaker identification. We reduce the number of test vectors by pre-quantizing the test sequence prior to matching, and the number of speakers by pruning out unlikely speakers during the identification process. The best variants are then generalized to Gaussian mixture model (GMM) based modeling also. We obtain a speed-up factor of 16:1 with VQ-based system, and 34:1 with GMM-based system with a minor degradation in the identification error rate.

/pdf/real-time-speaker-identification-3s35nh2ydz.pdf

Real-time speaker identification.

Problem statement: Accurate weather forecasting plays a vital role fo r planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological expert s. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network and radial basis function neural network we re used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis fu nction neural network is better than back propagation neural network.

/pdf/an-efficient-weather-forecasting-system-using-radial-basis-sk1e9ldn62.pdf

An Efficient Weather Forecasting System using Radial Basis Function Neural Network

Accurate weather predictions are important for planning our dayto-day activities. In recent years, a large literature has evolved on the use of artificial neural networks (ANNs) in many forecasting applications. Neural networks are particularly appealing because of their ability to model an unspecified non-linear relationship between weather variables. This paper evaluates three neural networks architectures with different training techniques, in this context: the popular multilayer perceptron (MLP), the radial basis function network (RBF) and feed forward neural networks which were trained by differential evolution algorithm. Different testing and training scenarios are presented. Those scenarios are designed to obtain the most suitable one for weather predication at different neural network architectures. Simulation results for each scenario demonstrate the effectives of both neural network architectures and its associated training algorithm.

/pdf/neural-networks-training-based-on-differential-evolution-g7k7rsmqiq.pdf

Neural Networks Training Based on Differential Evolution Algorithm Compared with Other Architectures for Weather Forecasting34

A comparative study of MLP-based artificial neural networks in text-independent speaker verification against GMM-based systems.

Application of Neural Networks to Weather Forecasting with Local Data.

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A comparative study of MLP-based artificial neural networks in text-independent speaker verification against GMM-based systems.

Application of Neural Networks to Weather Forecasting with Local Data.