This paper aims in comparing countries with different energy strategies, and demonstrate the close connection between environment and economic growth in the ex-Eastern countries, during their transition to market economies. We have developed a radial-basis function neural network system, which is trained to classify countries based on their emissions of carbon, sulphur and nitrogen oxides, and ...

Detection of rain/no-rain condition on the ground is an important for application of radar rainfall algorithms. A radial basis function (RBF) neural network-based scheme for rain/no-rain determination on the ground using vertical profiles of radar data is described in this paper. Evaluation based on WSR-88D radar over central Florida indicates that rain/no-rain condition can be inferred fairly ...

This paper, two Artificial Neural Network (ANN) models using radial basis function (RBF) nets are developed for the design of Aperture Coupled Microstrip Antennas (ACMSA) for different number of design parameters. The effect of increasing the number of design parameters on the ANN model is also discussed in this work. The performances of the models when compared are found that on decreasing the...

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles Anugrah K. Pamosoaji a , Pham Thuong Cat b & Keum-Shik Hong a c a School of Mechanical Engineering, Pusan National University, Busan, Korea b Department of Automation Technology, Institute of Information Technology, Hanoi, Vietnam c Department of Cogno-Mec...

The paper examines applicability of Hopfield Model (HFM) for weather forecasting in southern Saskatchewan, Canada. The model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN) and radial basis function network (RBFN). The data of temperature, wind speed and relative humidity were used to train and test the four models. With each model, ...

Neural networks are investigated for predicting the magnitude of the largest seismic event in the following month based on the analysis of eight mathematically computed parameters known as seismicity indicators. The indicators are selected based on the Gutenberg-Richter and characteristic earthquake magnitude distribution and also on the conclusions drawn by recent earthquake prediction studies...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

In this communication, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dim...

This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC scheme and applied to engine speed track...