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 ...

In this article experimental work is presented on the identification of the damping parameters in a new defence system, called Minesniper. Simulations have revealed that the standard identification model for the damping parameters with a linear and a quadratic term yields inaccurate results. Therefore, neural networks are used to represent the damping. As the available, noisy training data set ...

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...

This paper introduces a novel method for human face recognition that employs a set of different kind of features from the face images with Radial Basis Function (RBF) neural network called the Hybrid N-Feature Neural Network (HNFNN) human face recognition system. The face image is projected in each appropriately selected transform methods in parallel. The output of the RBF classifiers are fused...

Query expansion methods have been extensively studied in information retrieval. This paper proposes a query expansion method. The HQE method employs a combination of ontology-based collaborative filtering and neural networks to improve query expansion. In the HQE method, ontology-based collaborative filtering is used to analyze semantic relationships in order to find the similar users, and the ...

This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types processing their rotor sounds. The first method uses an optimum learning rate in each iteration of train process. This method increases the speed of learning process and al...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

This paper presents a novel algorithm that modifies the speech uttered by a source speaker to sound as if produced by a target speaker. In particular, we address the issue of transformation of the vocal tract characteristics from one speaker to another. The approach is based on estimating spectral envelopes using radial basis function (RBF) networks, which is one of the well-known models of art...