In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input–output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should b...

In this paper a dynamically adaptive neural network architecture is investigated for robot behavior learning. Specifically, a so-called “Grow When Required” network (GWR) is used to dynamically cluster the sensor-motor training data for determining the centers of a radial basis function network (RBF), and then the RBF network is trained for acquiring and performing the required behaviors. We il...

In this work we want to analyse the behaviour of two classic Artificial Neural Network models respect to a data complexity measures. In particular, we consider a Radial Basis Function Network and a MultiLayer Perceptron. We examine the metrics of data complexity known as Measures of Separability of Classes over a wide range of data sets built from real data, and try to extract behaviour pattern...

| This paper is composed of two parts. The rst part surveys the literature regarding optimum nonlinear l-tering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear ltering. In particular, the results obtained by using a regularized form of radial basis function (R...

Even though radial basis function networks are known to have good prediction accuracy in several domains, it is not known to decide a proper sample size like other data mining algorithms, so the task of deciding proper sample sizes for the networks tends to be arbitrary. As the size of samples grows, the improvement in error rates becomes better slowly. But we cannot use larger and larger sampl...

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...

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and visio...

A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); t...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

The selection of centers and widths has a strong influence on the performance of radial basis function neural network classifier. In this paper, a novel approach of clustering based on Fuzzy Cmeans clustering is proposed, which is called cooperative clustering, and use it for selection of centers of radial basis function neural network. Experimental results show that the performance of classifi...