44,867 research outputs found
Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks
This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking
A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module
The design process of photovoltaic (PV) modules can be greatly enhanced by
using advanced and accurate models in order to predict accurately their
electrical output behavior. The main aim of this paper is to investigate the
application of an advanced neural network based model of a module to improve
the accuracy of the predicted output I--V and P--V curves and to keep in
account the change of all the parameters at different operating conditions.
Radial basis function neural networks (RBFNN) are here utilized to predict the
output characteristic of a commercial PV module, by reading only the data of
solar irradiation and temperature. A lot of available experimental data were
used for the training of the RBFNN, and a backpropagation algorithm was
employed. Simulation and experimental validation is reported
Variable neural networks for adaptive control of nonlinear systems
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example
Predicting Fraud in Mobile Phone Usage Using Artificial Neural Networks
Mobile phone usage involves the use of wireless communication devices that can be carried
anywhere, as they require no physical connection to any external wires to work. However, mobile
technology is not without its own problems. Fraud is prevalent in both fixed and mobile networks of all
technologies. Frauds have plagued the telecommunication industries, financial institutions and other
organizations for a long time. The aim of this research work and research publication is to apply 3
different neural network models (Fuzzy, Radial Basis and the Feedforward) to the prediction of fraud in
real-life data of phone usage and also analyze and evaluate their performances with respect to their
predicting capability. From the analysis and model predictability experiment carried out in this scientific
research work, it was discovered that the fuzzy network model had the minimum error generated in its
fraud predicting capability. Thus, its performance in terms of the error generated in this fraud prediction
experiment showed that its NMSE (Normalized mean squared error) for the fraud predicted was
1.98264609. The mean absolute error (M AE = 15.00987244) for its fraud prediction was also the least;
this showed that the fuzzy model fraud predictability was much better than the other two models
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Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data. © 2004 Elsevier B.V. All rights reserved
An on-line training radial basis function neural network for optimum operation of the UPFC
The concept of Flexible A.C. Transmission Systems (FACTS) technology was developed to enhance the performance of electric power networks (both in steady-state and transient-state) and to make better utilization of existing power transmission facilities. The continuous improvement in power ratings and switching performance of power electronic devices together with advances in circuit design and control techniques are making this concept and devices employed in FACTS more commercially attractive. The Unified Power Flow Controller (UPFC) is one of the main FACTS devices that have a wide implication on the power transmission systems and distribution. The purpose of this paper is to explore the use of Radial Basis Function Neural Network (RBFNN) to control the operation of the UPFC in order to improve its dynamic performance. The performance of the proposed controller compares favourably with the conventional PI and the off-line trained controller. The simple structure of the proposed controller reduces the computational requirements and emphasizes its appropriateness for on-line operation. Real-time implementation of the controller is achieved through using dSPACE ds1103 control and data acquisition board. Simulation and experimental results are presented to demonstrate the robustness of the proposed controller against changes in the transmission system operating conditions
Identification of Nonlinear Systems Using Radial Basis Function Neural Network
This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian
function
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