This paper describes a global approach to the construction of Radial Basis Function (RBF) neural net classifier. We used a new simple algorithm to completely define the structure of the RBF classifier. This algorithm has the major advantage to require only the training set (no step learning, threshold or other parameters as in other methods). Tests on several benchmark datasets showed, despite ...

The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: 1) the necessary and sufficient condition for a function of one variable to be qualified as an activation function in RBF network is that the function is not an even polynomial, and 2) the capability of approximation to nonlinear functionals and operators b...

Partial differential equations (PDEs) with boundary conditions (Dirichlet or Neumann) defined on boundaries with simple geometry have been successfully treated using sigmoidal multilayer perceptrons in previous works. This article deals with the case of complex boundary geometry, where the boundary is determined by a number of points that belong to it and are closely located, so as to offer a r...

The main aim of this paper is to determine to analyze the electrical transfer capability among different electricity markets using repeated power flow technique. Instead of minimizing the total cost in the conventional problem, in the paper, the transfer capability between two markets or two electricity supply and generation areas is maximized. To reduce the time required to compute transfer ca...

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 ...

Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four di erent models of Arti cial Neural Networks (ANN): Backpropagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classi cation of patients of mild Alzheimer's disease vs. control...

Aquatic ecosystems are facing a growing number of human induced changes and threats. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensity of human expert taxonomic id...

In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model’s performance with some existing netwo...

In order to improve the precision of gyroscope, two decoupling method of DTG(Dynamic Tuned Gyroscope) were analyzed, the BP neural network and RBF network. The BP neural network has many advantages Compared to the traditional decoupling method, but still some drawbacks such as the over training, the congress process is very slow, and the hidden layer is also hard to determined. The paper introd...

The main contribution of the present paper is to train efficient neural networks for seismic design of double layer grids subject to multiple-earthquake loading. As the seismic analysis and design of such large scale structures require high computational efforts, employing neural network techniques substantially decreases the computational burden. Square-on-square double layer grids with the va...