this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

This paper shows the latest results of the EvRBF algorithm applied to classification problems. EvRBF is an evolutionary algorithm intended to automatically design Radial Basis Functions Neural Networks (RBFNN), being its main advantage that establishes the whole set of parameters of the nets. Key–Words: Radial basis function neural networks, evolutionary algorithms, evolutionary operators,EvRBF...

This paper proposes an artificial neural network RBF to classification using feature descriptors. The theoretical and practical aspects of theory F distributions with different degrees of freedom introduced. The distribution F densities are similar in shape, making it difficult to identify the differences between the two densities. This paper is concerned with separating these same probability ...

Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astroand geophysics as well as meteorology. Managing a continuous flow of data, automisation of the detection and classification process is important. Features based on a combination of wavelet a...

Using RBF units in neural networks are very interesting option that make network more powerful. The paper presents new training algorithm based on second order ErrCor algorithm. The effectiveness of proposed algorithm has been confirmed by several experiments.

With the evolvement of fMRI’s, a great amount of attention has been given to classifying cognitive states of human beings. Several machine learning approaches have been used to train single-subject classifiers to do so. We present a different method using a neural network and a RBF SVM to train one classifier across all subjects. For the single-subject classifier case, we experiment with PCA as...

We present a useful and effective fingerprint image segmentation. We extract two new features with which our algorithm can distinguish noisy area from the foreground, and, therefore, can reduce the number of false minutiae. We use supervised RBF neural network to classify patterns and select typical patterns to train the classifier. Experimental results show a significant improvement in fingerp...

| This paper presents the Time Delay Radial Basis Function Network (TDRBF) for recognition of pho-nemes. The TDRBF combines features from Time Delay Neural Networks (TDNN) and Radial Basis Functions (RBF). The ability to detect acoustic features and their temporal relationship independent of position in time is inherited from TDNN. The use of RBFs leads to shorter training times and less parame...

In any neural network system, proper parameter initialization reduces training time and effort, and generally leads to compact modeling of the process under examination, i.e. less complex network structures and better generalization. However, in cases of multi-dimensional data, parameter initialization is both difficult and time consuming. In the proposed scheme a novel, multi-dimensional, unsu...

One of the main research concern in neural networks is to find the appropriate network size in order to minimize the trade-off between overfitting and poor approximation. In this paper the choice among different competing models that fit to the same data set is faced when statistical methods for model comparison are applied. The study has been conducted to find a range of models that can work a...