Adaptive Control of Multiagent Systems for Finding Peaks of Uncertain Static Fields

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Mahdi Jadaliha ◽  
Joonho Lee ◽  
Jongeun Choi
Keyword(s):  
Adaptive Control ◽  
Multiagent Systems ◽  
Radial Basis Function Network ◽  
Distributed Coordination ◽  
Convergence Properties ◽  
Extensive Simulation ◽  
Coordination Algorithms ◽  
Sampling Algorithms ◽  
Simulation Results ◽  
Function Network

In this paper, we design and analyze a class of multiagent systems that locate peaks of uncertain static fields in a distributed and scalable manner. The scalar field of interest is assumed to be generated by a radial basis function network. Our distributed coordination algorithms for multiagent systems build on techniques from adaptive control. Each agent is driven by swarming and gradient ascent efforts based on its own recursively estimated field via locally collected measurements by itself and its neighboring agents. The convergence properties of the proposed multiagent systems are analyzed. We also propose a sampling scheme to facilitate the convergence. We provide simulation results by applying our proposed algorithms to nonholonomic differentially driven mobile robots. The extensive simulation results match well with the predicted behaviors from the convergence analysis and illustrate the usefulness of the proposed coordination and sampling algorithms.

Sampled-Data Indirect Adaptive Control of Bioreactor Using Affine Radial Basis Function Network Architecture

1997 ◽  
Vol 119 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Dimitry Gorinevsky
Keyword(s):  
Adaptive Control ◽  
Nonlinear System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Control Algorithms ◽  
Sampled Data ◽  
Radial Basis ◽  
Radial Basis Function Approximation ◽  
Function Network

This paper considers a problem of bioreactor control, which is formulated in Anderson and Miller (1990) and Ungar (1990) as a benchmark problem for application of neural network-based adaptive control algorithms. A completely adaptive control of this strongly nonlinear system is achieved with no a priori knowledge of its dynamics. This becomes possible thanks to a novel architecture of the controller, which is based on an affine Radial Basis Function network approximation of the sampled-data system mapping. Approximation with such net-work could be considered as a generalization of a standard practice to linearize a nonlinear system about the working regime. As the network is affine in the control components, it can be inverted with respect to the control vector by using fast matrix computations. The considered approach includes several features, recently introduced in some advanced process control algorithms. These features—multirate sampling, on-line adaptation, and Radial Basis Function approximation of the system nonlinearity—are crucial for the achieved high performance of the controller.

scholarly journals Brain Tumor Classification by EGSO Based RBFNN Classifier

2020 ◽  
Vol 8 (5) ◽  
pp. 3005-3012
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Mri Brain ◽  
Radial Basis ◽  
The Mean ◽  
Simulation Results ◽  
Group Search ◽  
Function Network ◽  
The Ideal

Tumor classifier is modelled employing a proposed Enhanced Group Search Optimizer based Radial Basis Function Neural Network model is applied in this research contribution to acquire the ideal instances from the developed VOI instance an as well EGSO is utilized to optimize the weight values of the Radial Basis Function Network classifier by limiting the mean square mistake. The anticipated EGSO based RBFNN classifier brings better characterization precision and accomplished insignificant error with quicker process. The simulation results computed prove the effectiveness of the RBFNN classifier to be better in comparison with the other proposed classifiers in this thesis and that available in the literature. The proposed pattern evaluation technique presents an automatic cancer categorization procedure thru the ultimate facets which fantastic characterizes MRI brain image is benign and malignant cancers. The planned method may perhaps stretch to categorize exceptional classes of tumor (eg. Meningioma, glioma etc.,) and depth of malignancy.

Study on Optimization of Software Regressive Testing Based on RBF Neural Networks

2012 ◽  
Vol 268-270 ◽  
pp. 1714-1717 ◽  
Author(s):  
Wen Le Bai ◽  
Yong Mei Zhang ◽  
Bin Song
Keyword(s):  
Neural Networks ◽  
Basis Function ◽  
Learning Strategy ◽  
Radial Basis Function Network ◽  
Regression Testing ◽  
Rbf Neural Networks ◽  
Simulation Results ◽  
Adaptive Ability ◽  
New Research ◽  
Function Network

In order to reduce times of software regression testing, a new research idea and method is proposed based on RBFN (Radial Basis Function Network). Using the adaptive ability of network study, regression testing is optimized by its learning strategy. The simulation results demonstrate the new method can forecast regressive testing effectively, and implement very little error. It means an important meaning for developing new effective method of soft testing in the future.

Adaptive Control of a Galvannealing Process with a Radial Basis Function Network

1999 ◽  
Vol 32 (2) ◽  
pp. 7179-7184
Author(s):  
Christian Schicfer ◽  
H. Peter Jögl ◽  
Franz X. Rubenzucker ◽  
Heinrich Aberl
Keyword(s):  
Adaptive Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Radial Basis ◽  
Function Network

scholarly journals Optimization of transmission signal by artificial intelligent

2019 ◽  
Vol 8 (4) ◽  
pp. 290
Author(s):  
Hassan Farahan Rashag ◽  
Mohammed H. Ali
Keyword(s):  
Communication System ◽  
System Performance ◽  
Radial Basis Function Network ◽  
Activation Functions ◽  
Artificial Intelligent ◽  
Communication System Performance ◽  
Input Layer ◽  
Simulation Results ◽  
Hidden Layer ◽  
Function Network

<p>In this method, radial basis function network RBFNN is an artificial intelligent which is used to identify and classify the communication system performance.  RBFNN is one type of neural network which has activation functions. It consists of three layer input layer, hidden layer and output linear combination. One of the main problems of communication system is that it causes slow response for sending signal via the transmission devices. Therefore, the artificial intelligent by RBFNN is used to optimize the transmission signal. The input signal is trained and testing by neurons with weight and this lead to provide linear output. The simulation results have the optimization specifics over the traditional communication transmission devices.</p>

Adaptive Control of Chaotic Systems with Uncertainties

1998 ◽  
Vol 08 (10) ◽  
pp. 2041-2046 ◽  
Author(s):  
Huaizhou Zhang ◽  
Huashu Qin ◽  
Guanrong Chen
Keyword(s):  
Adaptive Control ◽  
Chaotic Systems ◽  
Radial Basis Function Network ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Control Scheme ◽  
On Line ◽  
Uncertain Chaotic Systems ◽  
Adaptation Law ◽  
Function Network

In this paper, an adaptive control scheme, that employs a Gaussian radial basis function network with output weights updated on-line according to the Lyapunov stability theory, is suggested for regulation of a class of chaotic systems with uncertainties. Theoretical analysis guarantees that under the control of the proposed adaptation law, uncertain chaotic systems can asymptoticaly track target orbits within arbitrarily small tolerance bounds. As an example, control of the uncertain Duffing–Holmes system is presented with computer simulations, which verifies and visualizes the theory and design of the adaptive controller.

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