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...

A feedback simulation model based on radial basis function neural networks is newly developed in this research to analyse the interaction between urban densities and travel mode split. The changes of populations, road mileages, travel mode split, and so on of the enlarging urbanized areas of different cities in China are studied for the trainings of the radial basis function neural networks con...

In this paper it is proposed an image compression method based on the idea of fitting a set of Neural Networks (NNs) outputs to the image surface, which is a three-dimensional surface where the pixel values are considered as heights (z-values) defined on the x–y ground plane. An image is divided into subimages (blocks) using a quad tree, according to the complexity of the image surface. Individ...

Neural networks are powerful computational tools, and have been applied in various applications. In this work, a neural network has been used to solve a pattern classification problem encountered in biochemistry. One of the major topics of research in molecular biology is the prediction of functional properties of biomacromolecules from their sequence data. A radial basis function (RBF) network...

Finance and investing is the second most frequent business area of neural networks applications after production/operations. Although many research results show that neural networks can solve almost all problems more efficiently than traditional modeling and statistical methods, there are opposite research results showing that statistical methods in particular data samples outperform neural net...

Even though multilayer perceptrons and radial basis function networks belong to the class of artificial neural networks and they are used for similar tasks, they have very different structures and training mechanisms. So, some researchers showed better performance with radial basis function networks, while others showed some different results with multilayer perceptrons. This paper compares the...

The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acce...