Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among whi...

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering o...

In many high-level signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for modeling the relationships among sets of spectral envelopes. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from ...

The A60 class deck penetration piece is a fire-resistance apparatus installed on the compartment to protect lives and prevent flame diffusion in case of fire accident ship or offshore plant. In this study, sensitivity performance approximation characteristics for was evaluated by conducting transient heat-transfer analysis test. conducted evaluate design piece, results were verified via penetra...

This paper presents a comparative analysis of different connectionist and statistical models for forecasting the weather of Vancouver, Canada. For developing the models, one year’s data comprising of daily temperature and wind speed were used. A multi-layered perceptron network (MLPN) and an Elman recurrent neural network (ERNN) were trained using the one-step-secant and Levenberg–Marquardt alg...

In this article we discuss artificial neural networks-based fault detection and isolation applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, ...

This paper proposes a fault diagnosing system for the Ice-Storage Air-Conditioning System (ISACS) to supervise operation conditions of brine chillers. Combining Radial Basis Function Network (RBFN) and Robust Quality Design (RQD), an Enhanced RBFN (ERBFN) is proposed pursue fast accurate diagnosis. The RQD method used adjust parameters in training stage improve searching ability, good performan...

In this paper we have applied radial basis function neural networks for equalization of wireless channel in the presence of additive noise. We have been tested the adaptive filter algorithms of simple Kalman filter, extended Kalman filter, rbfn Kalman filter, rbfn extended Kalman filter, fuzzy Kalman filter and fuzzy extended Kalman filter which might be considered applicable for wireless chann...

In the speed sensorless vector control system, the amended method of estimating the rotor speed about model reference adaptive system (MRAS) based on radial basis function neural network (RBFN) for PMSM sensorless vector control system was presented. Based on the PI regulator, the radial basis function neural network which is more prominent learning efficiency and performance is combined with M...

In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental interference cancellation of speech signal. We show that the Second Order ThinPlate Spline (SOTPS) kernel cancels the interferences effectively. For make comparison, we test our experiments on two conventional most used RBFN kernels: the Gaussian and First order TPS (FOTPS) basis functions. The ...