In this paper, Multi-Layer Perceptron and RadialBasis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3and 6-h lead time prediction and the only reliable one for 9-h lead time forecas...

In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...

Partial differential equations (PDEs) with Dirichlet boundary conditions defined on boundaries with simple geomerty have been succesfuly treated using sigmoidal multilayer perceptrons in previous works [1, 2]. 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 reasonabl...

A boosting-based method for centers placement in radial basis function networks (RBFN) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFN is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer Perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learn...

C o m p a ra t iv e S tu d y of th e C a s c a d e-C o r re la t io n A rc h ite c tu re in P a t te r n R e c o g n itio n A p p lic a tio n s Abstract In this work, an experimental evaluation of the cascade-correlation architecture is carried out in different bench-marking pattern recognition problems. An extensive experimental framework is developed to establish a comparison between the casc...

In this paper we propose several sets of new features for protein fold prediction. The first feature set consisting of 47 features uses only the sequence information. We also define four different sets of features based on hydrophobicity of amino acids. Each such set has 400 features which are motivated by folding energy modeling. To define these features we have considered pair-wise amino acid...

In this paper, we suggest a neural network signal detector using radial basis function (RBF) network. We employ this RBF Neural detector to detect the presence or absence of a known signal corrupted by different Gaussian, non-Gaussian and impulsive noise components. In case of non-Gaussian noise, experimental results show that RBF network signal detector has significant improvement in performan...

Hierarchical mixtures of experts (HME) [JJ94] and radial basis function (RBF) networks [PG89] are two architectures that learn much faster than multilayer perceptrons. Their faster learning is due not to higherorder search mechanisms, but to restricting the hypothesis space of the learner by constraining some of the layers of the network to use linear processing units. It can be conjectured tha...