Abstract Hysteresis widely exists in civil structures, and dissipates the mechanical energy of systems. Research on random vibration hysteretic systems, however, is still insufficient, particularly when excitation non-Gaussian. In this paper, radial basis function (RBF) neural network (RBF-NN) method adopted as a numerical to investigate Bouc-Wen system under Poisson white noise excitations. Th...

This paper presents a novel implementation of the RBFDDA (Radial Basis Functions Dynamic Decay Adjustment) neural network. We will investigate the architectural design and the hardware implementation. The features of this design are the possibility of mapping the architecture on different standard cells libraries as well as FPGA's, and mainly its modularization. This is an example which reflect...

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...

To seek optimal network parameters of Radial Basis Function (RBF) Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial ...

Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian ...

Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major pr...

In this paper, we present a novel technique for restoring a blurred noisy image without any prior knowledge of the blurring function and the statistics of noise. The technique combines wavelet transform with radial basis function (RBF) neural network to restore the given image which is degraded by Gaussian blur and additive noise. In the proposed technique, the wavelet transform is adopted to d...

This paper is intender to be a simple example illustrating some of the capabilities of Radial basis function by pruning with QLP decomposition. The applicability of the radial basis function (RBF) type function of artificial neural networks (ANNS) approach for re-estimate the Box, Traingle, Epanechnikov and Normal densities. We propose an application of QLP decomposition model to reduce to the ...

In this paper, the chaotic time series RBF neural network model was designed. A prediction method for underwater acoustic chaotic signal based on RBF neural network is proposed in this paper according to the characteristics of chaotic signal with the short-term prediction. Typical Henon chaotic signal and the actual underwater acoustic chaotic signal are respectively predicted by the RBF neural...

Standard particle swarm optimization (SPSO) algorithm was modified by escape strategy of the particle velocity, and an escape PSO (EPSO) was proposed to overcome the shortcomings of being trapped in local optima because of premature convergence. To enhance the performance of radial basis function (RBF) neural network, the EPSO is combined with RBF neural network to form a EPSON hybrid algorithm...