In the present study, the reliability assessment of performance-based optimally seismic designed reinforced concrete (RC) and steel moment frames is investigated. In order to achieve this task, an efficient methodology is proposed by integrating Monte Carlo simulation (MCS) and neural networks (NN). Two NN models including radial basis function (RBF) and back propagation (BP) models are examine...

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

Function Equalizer Design Sarat Kumar Patra , Bernard Mulgrew Dept of Electrical Engineering, University of Edinburgh, Edinburgh, EH9 3JL, UK. Tel: +44 131 6505655; fax: +44 131 650 6554 e-mail: [email protected] ABSTRACT This paper investigates the computational aspects of radial basis function (RBF) equalizers. In an RBF implementation of the Bayesian equalizer the RBF centers are placed at equ...

A meshfree approximation scheme based on the radial basis function methods is presented for the numerical solution of the options pricing model. This thesis deals with the valuation of the European, Barrier, Asian, American options of a single asset and American options of multi assets. The option prices are modeled by the Black-Scholes equation. The θ-method is used to discretize the equation ...

Presents a systematic approach for constructing reformulated radial basis function (RBF) neural networks, which was developed to facilitate their training by supervised learning algorithms based on gradient descent. This approach reduces the construction of radial basis function models to the selection of admissible generator functions. The selection of generator functions relies on the concept...

SUMMARY Representing the concept of numerical data by linguistic rules is often desir­ able. In this paper, we present a novel rule-extraction algorithm from the radial basis function (RBF) neural network classifier for representing the hidden concept of numerical data. Gaussian function is used as the basis function of the RBF network. When training the RBF neural network, we allow for large o...

Ratio pulse arithmetic represents a signal with a random pulse stream and its value is defined as the ratio of ones and zeroes in its random pulse stream. We propose circuits for the exponential function and subtraction, and explain how to implement a radial basis function(RBF) network. We applied the RBF network to a classifier example. Simulation results show that its performance is comparabl...

Conventionally, in radial basis function (RBF) network width factor is constructed by obtaining r-nearest neighbor rule or taking equal to a constant for all Gaussian functions. This paper proposes an approach for the construction of width factor using genetic algorithm to optimize the Gaussian function. Our experimental results show that our proposed optimal-based width outperforms the convent...

We present a generalization of the RBF-FD method that allows full control of the overlap between RBF-FD stencils. We accomplish this by introducing a continuous overlap parameter δ ∈ [0, 1] such that δ = 1 recovers the standard RBF-FD method and δ = 0 results in a full decoupling of the RBF-FD stencils. We show with a simple example that global interpolation with both RBFs and RBFs augmented wi...

In this paper we develop a discrete Hierarchical Basis (HB) to efficiently solve the Radial Basis Function (RBF) interpolation problem with variable polynomial order. The HB forms an orthogonal set and is adapted to the kernel seed function and the placement of the interpolation nodes. Moreover, this basis is orthogonal to a set of polynomials up to a given order defined on the interpolating no...