Determination of probabilistic risk of voltage collapse using radial basis function (RBF) network

2006 ◽  
Vol 76 (6-7) ◽  
pp. 426-434 ◽  
Author(s):  
L.D. Arya ◽  
L.S. Titare ◽  
D.P. Kothari
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Collapse ◽  
Rbf Network ◽  
Radial Basis ◽  
Probabilistic Risk

A novel local radial basis function collocation method for multi-dimensional piezoelectric problems

2021 ◽  
pp. 1045389X2110572
Author(s):  
Amir Noorizadegan ◽  
Der Liang Young ◽  
Chuin-Shan Chen
Keyword(s):  
Radial Basis Function ◽  
Collocation Method ◽  
Basis Function ◽  
Shape Parameter ◽  
Piezoelectric Medium ◽  
The Finite Element Method ◽  
Mechanical Field ◽  
Radial Basis ◽  
2D And 3D

The local radial basis function collocation method (LRBFCM), a strong-form formulation of the meshless numerical method, is proposed for solving piezoelectric medium problems. The proposed numerical algorithm is based on the local Kansa method using variable shape parameter. We introduce a novel technique for the determination of shape parameter in the LRBFCM, which leads to greater accuracy, and simplicity. The implemented algorithm is first verified with a 2D Poisson equation. Then, we employed LRBFCM in a numerical simulation for 2D and 3D piezoelectric problems involving mutual coupling of the electric field and elastodynamic equations for mechanical field. The presented meshless method is verified using corresponding results obtained from the finite element method and moving least squares meshless local Petrov–Galerkin method. In particular, the 2D piezoelectric problem is verified with an exact solution.

Radial basis function neural network chaos control of a piezomagnetoelastic energy harvesting system

2019 ◽  
Vol 25 (16) ◽  
pp. 2191-2203 ◽  
Author(s):  
R. Dehghani ◽  
H. M. Khanlo
Keyword(s):  
Energy Harvesting ◽  
Radial Basis Function ◽  
Basis Function ◽  
Output Voltage ◽  
Chaos Control ◽  
Chaotic Behavior ◽  
Rbf Network ◽  
Magnetic Forces ◽  
Radial Basis ◽  
Energy Harvesting System

In this paper, an adaptive chaos control is proposed for a typical vibratory piezomagnetoelastic energy harvesting system to return the chaotic behavior to a periodic one. Piezomagnetoelastic energy harvesting systems show chaotic behaviors in spite of harmonic input. Although, the chaotic behavior of the system gives higher output voltage than the periodic motion, it is preferred to the output voltage as this is periodic for charging a battery or a capacitor efficiently. Therefore, the chaos control is important in this system. The physical model is composed of the upper and lower piezoelectric layers on a cantilever taper beam, one attached tip magnet, and two external magnets (EM). Position of the EM is controlled by inputs. Firstly, chaotic and periodic regions are detected by utilizing the bifurcation diagrams, phase plan portrait, and Poincaré maps. Then an adaptive controller is proposed for controlling of the chaotic behaviors in the presence of uncertainty due to magnetic forces. The control law is derived based on the inverse dynamic method and the uncertainty elements of the controller are estimated using radial basis function (RBF) network. The weights of the RBF network are obtained using an adaptation law. The adaptation laws are derived based on Lyapunov stability theory and a projection operator. The distance of the tip magnet and the EM as well as the gap distance of two EM are used to control the chaotic behavior. Simulation results show that the proposed controller can return the chaotic motion to a periodic one in spite of the uncertainties in the magnetic forces.

scholarly journals Radial Basis Function Network Based Design of Incipient Motion Condition of Alluvial Channels with Seepage

2010 ◽  
Vol 58 (2) ◽  
pp. 102-113 ◽  
Author(s):  
Bimlesh Kumar ◽  
Gopu Sreenivasulu ◽  
Achanta Rao
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Incipient Motion ◽  
Alluvial Channels ◽  
Rbf Network ◽  
Motion Condition ◽  
Radial Basis ◽  
Alluvial Channel ◽  
Function Network

Radial Basis Function Network Based Design of Incipient Motion Condition of Alluvial Channels with SeepageIncipient motion is the critical condition at which bed particles begin to move. Existing relationships for incipient motion prediction do not consider the effect of seepage. Incipient motion design of an alluvial channel affected from seepage requires the information about five basic parameters, i.e., particle sized, water depthy, energy slopeSf, seepage velocityvsand average velocityu.As the process is extremely complex, getting deterministic or analytical form of process phenomena is too difficult. Data mining technique, which is particularly useful in modeling processes about which adequate knowledge of the physics is limited, is presented here as a tool complimentary to model the incipient motion condition of alluvial channel at seepage. This article describes the radial basis function (RBF) network to predict the seepage velocity vs and average velocityubased on experimental data of incipient condition. The prediction capability of model has been found satisfactory and methodology to use the model is also presented. It has been found that model predicts the phenomena very well. With the help of the RBF network, design curves have been presented for designing the alluvial channel when it is affected by seepage.

Reconstructing cylinder pressure from vibration signals based on radial basis function networks

2001 ◽  
Vol 215 (6) ◽  
pp. 761-767 ◽  
Author(s):  
H Du ◽  
L Zhang ◽  
X Shi
Keyword(s):  
Radial Basis Function ◽  
Network Model ◽  
Basis Function ◽  
Cylinder Head ◽  
Cylinder Pressure ◽  
Vibration Signals ◽  
Rbf Network ◽  
Engine Cylinder ◽  
Wide Range ◽  
Radial Basis

This paper presents an approach to reconstruct internal combustion engine cylinder pressure from the engine cylinder head vibration signals, using radial basis function (RBF) networks. The relationship between the cylinder pressure and the engine cylinder head vibration signals is analysed first. Then, an RBF network is applied to establish the non-parametric mapping model between the cylinder pressure time series and the engine cylinder head vibration signal frequency series. The structure of the RBF network model is presented. The fuzzy c-means clustering method and the gradient descent algorithm are used for selecting the centres and training the output layer weights of the RBF network respectively. Finally, the validation of this approach to cylinder pressure reconstruction from vibration signals is demonstrated on a two-cylinder, four-stroke direct injection diesel engine, with data from a wide range of speed and load settings. The prediction capabilities of the trained RBF network model are validated against measured data.

Approximation by Growing Radial Basis Function Networks Using the Differential-Evolution-Based Algorithm

2005 ◽  
Vol 9 (5) ◽  
pp. 540-548
Author(s):  
Junhong Liu ◽  
◽  
Jouni Lampinen
Keyword(s):  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Network ◽  
Local Tuning ◽  
De Algorithm ◽  
Network Training ◽  
Radial Basis ◽  
Network Output ◽  
Growth Method

The differential evolution (DE) algorithm is a floating-point-encoded evolutionary algorithm for global optimization. We applied a DE-based method to training radial basis function (RBF) networks with variables including centers, weights, and widths. This algorithm consists of three steps – initial tuning focusing on finding the center of a one-node RBF network, local tuning, and global tuning both using cycling schemes to find RBF network parameters. The mean square error from desired output to actual network output is applied as the objective function to be minimized. Network training is shown by approximating a set of functions and reconstructing the spectra of oil samples and classification. Net performance is compared to approaches reported in the literature, and the resulting network generally performs better based on the tests performed. Results show that DE-based Gaussian RBF growth method improves approximation results reported.

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