The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily ba...

the present study is an attempt to investigate some features of radial basis functions (rbfs) approximation methods related to variational problems. thereby authors applied some properties of rbfs to develop a direct method which reduces constrained variational problem to a static optimization problem. to assess the applicability and effectiveness of the method, some examples are examined. dyna...

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit’s centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automat...

abstract in this research, a radial basis function artificial neural network (rbf-ann) model was developed to predict the hot deformation flow curves of api x65 pipeline steel. the results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of zener-hollomon parameter and a third order polynomial functio...

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find ...

Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial functio...

This paper considers the modeling and control problem for nonstationary nonlinear systems whose dynamic characteristics depend on time-varying working-points and may be locally linearized. It is proposed to describe the system behavior by the RBFARX model, which is an ARX model with Gaussian radial basis function (RBF) network-style coefficients depending on the working-points of a system. The ...

In this review we bring together some of our recent work from the angle of the diversified RBF topologies, including three different topologies; (i) the RBF network with tunable nodes; (ii) the Box-Cox output transformation based RBF network (Box-Cox RBF); and (iii) the RBF network with boundary value constraints (BVC-RBF). We show that the modified topologies have some advantages over the conv...

The identification of nonlinear systems by artificial neural networks has been successfully applied in many applications. In this context, the radial basis function neural network (RBF-NN) is a powerful approach for nonlinear identification. A RBF neural network has an input layer, a hidden layer and an output layer. The neurons in the hidden layer contain Gaussian transfer functions whose outp...

By visual inspection, both models produce good quality RBF images. However, comparison of regional averages to RBF values that were calculated from regional TACs (Figure C1), show that RBV must be included in the model (three-parameter model) if similar RBF estimates are required. RBF may be very sensitive to RBV, and if RBV is not fitted as the third parameter, or not corrected based on [O]CO ...