scholarly journals Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks

1999 ◽  
Vol 65 (10) ◽  
pp. 4404-4410 ◽  
Author(s):  
M. F. Wilkins ◽  
Lynne Boddy ◽  
C. W. Morris ◽  
R. R. Jonker
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Training Data ◽  
Measured Parameter ◽  
Rbf Networks ◽  
Rbf Network ◽  
Flow Cytometry Data ◽  
Freshwater Phytoplankton ◽  
Flow Cytometric ◽  
Radial Basis

ABSTRACT We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91.5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from “novel” species (species not present in the training data) were analyzed.

RADIAL BASIS FUNCTION NETWORKS AS CHAOTIC GENERATORS FOR SECURE COMMUNICATION SYSTEMS

1999 ◽  
Vol 09 (01) ◽  
pp. 221-232 ◽  
Author(s):  
S. PAPADIMITRIOU ◽  
A. BEZERIANOS ◽  
T. BOUNTIS
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Secure Communication ◽  
Communication Systems ◽  
Chaotic Systems ◽  
Chaotic Maps ◽  
Training Data ◽  
Radial Basis Function Networks ◽  
Rbf Networks ◽  
Radial Basis

This paper improves upon a new class of discrete chaotic systems (i.e. chaotic maps) recently introduced for effective information encryption. The nonlinearity and adaptability of these systems are achieved by designing proper radial basis function networks. The potential for automatic synchronization, the lack of periodicity and the extremely large parameter spaces of these chaotic maps offer robust transmission security. The Radial Basis Function (RBF) networks offer a large number of parameters (i.e. the centers and spreads of the RBF kernels and the weights of the linear layer) while at the same time as universal approximators they have the flexibility to implement any function. The RBF networks can learn the dynamics of chaotic systems (maps or flows) and mimic them accurately by using many more parameters than the original dynamical recurrence. Since the parameter space size increases exponentially with respect to the number of parameters, the RBF based systems greatly outperform previous designs in terms of encryption security. Moreover, the learning of the dynamics from data generated by chaotic systems guarantees the chaoticity of the dynamics of the RBF networks and offers a convenient method of implementing any desirable chaotic dynamics. Since each sequence of training data gives rise to a distinct RBF configuration, theoretically there exists an infinity of possible configurations.

Universal Approximation Using Radial-Basis-Function Networks

1991 ◽  
Vol 3 (2) ◽  
pp. 246-257 ◽  
Author(s):  
J. Park ◽  
I. W. Sandberg
Keyword(s):  
Finite Number ◽  
Radial Basis Function ◽  
Basis Function ◽  
Universal Approximation ◽  
Radial Basis Function Networks ◽  
Feedforward Networks ◽  
Rbf Networks ◽  
Smoothing Factor ◽  
Radial Basis ◽  
Hidden Layer

There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

Fast Learning in Neural Networks

2008 ◽  
pp. 91-104
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Continuous Function ◽  
Radial Basis Function ◽  
Supervised Learning ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Rbf Networks ◽  
Rbf Network ◽  
Fast Learning

We noted in the previous chapters that, while the multilayer perceptron is capable of approximating any continuous function, it can suffer from excessively long training times. In this chapter we will investigate methods of shortening training times for artificial neural networks using supervised learning. (Haykin, 1999) is a particularly good reference for radial basis function, RBF, networks. In this chapter we outline the theory and implementation of a RBF network before demonstrating how such a network may be used to solve one of the previously visited problems, and compare our solutions.

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.

Radial Basis Function Network Based MPPT for Photovoltaic System during Shading Condition

2013 ◽  
Vol 448-453 ◽  
pp. 1474-1479
Author(s):  
Mahamad Abd Kadir ◽  
Saon Sharifah
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Variable Temperature ◽  
Radial Basis Function Network ◽  
Photovoltaic System ◽  
Pv System ◽  
Rbf Network ◽  
Point Tracking ◽  
Radial Basis ◽  
Temperature Constant

The output powers of photovoltaic (PV) system are crucially depending of the two variable factors, which are the cell temperatures and solar irradiances. A method to utilize effectively the PV is known as a maximum power point tracking (MPPT) method. This method is extract the maximum available power from PV module by making them operates at the most efficient output. This paper presents Radial Basis Function (RBF) Network to control the MPPT of PV system. The performances of the controller is analyzed in four conditions with are constant irradiation and temperature, constant irradiation and variable temperature, constant temperature and variable irradiation, and variable temperature and irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, RBF controller has shown better performance during partially shaded conditions.

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