The Root Mean Square-Deviation (RMSD) or Root Mean Square Error (RMSE) is the frequently used measure of the difference between values predicted by a model or an estimator and the values actually observed from that which is being modelled or estimated. In this paper, we show that the magnification of the RMSE, when used with the classifier Hopfield Neural Network (HNN), may help the network to ...

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power meas...

This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back-Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for predicting the Stock Price Indices for one day, one week, two weeks and one month in advance. The Indian stock prices i.e. BSE (Bomba...

In this study, several regression models were employed to estimate global solar radiation from commonly available meteorological data such as sunshine duration, temperature, precipitation, and cloud cover for 34 meteorological stations of Bangladesh. The models studied were calibrated using five meteorological stations that are providing global solar radiation as well as other meteorological da...

Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP.

In other words, find the probability density functions f(·) and g(·) in (2) corresponding to the model (1). (b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates....

In other words, find the probability density functions f(·) and g(·) in (2) corresponding to the model (1). (b) Simulate the model (1) to produce T = 100 measurements y1:T . Based on these measurements compute the optimal (in the sense that it minimizes the mean square error) estimate of xt | y1:t for t = 1, . . . , T . Implement a bootstrap particle filter and compare to the optimal estimates....

This paper presents the application of feed-forward multilayer perceptron networks to forecast hourly nitrogen oxides levels 24 hours in advance. Input data were meteorological variables, average hourly traffic and nitrogen oxides hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles) was analyzed in order to improve the models’ predi...