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2 edition of Adaptive noise cancellation using recurrent radial basis function networks found in the catalog.

Adaptive noise cancellation using recurrent radial basis function networks

S. A. Billings

Adaptive noise cancellation using recurrent radial basis function networks

by S. A. Billings

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  • 26 Currently reading

Published by University of Sheffield, Dept. of Automatic Control and Systems Engineering in Sheffield .
Written in English


Edition Notes

Statement S.A. Billings and C.F. Fung.
SeriesResearch report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.472, Researchreport (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.472.
ContributionsFung, C. F.
ID Numbers
Open LibraryOL13968166M

Different adaptive PID controllers [5, 6] like fuzzy adaptive PID controllers [3], adaptive PID controllers based on neural networks [4] and evolutionary algorithms [2] have attracted a lot of attention in recent years. Needing to have the least knowledge of the controlled plant is one of the priorities of these kinds of controllers. occurs and we will explain that in detail in Chapter 3. This is why radial basis functions always de fine a space S ⊂ C (Rn) which depends on. The simplest example is, for a finite set of centres in Rn, () S = ξ∈ λξ− ξ λ ξ ∈ R. Here the ‘radial basis function’ is simply φ(r)= r, the radial symmetry stem-.

ADAPTIVE RADIAL BASIS FUNCTION NEURAL NETWORKS-BASED REAL TIME HARMONICS ESTIMATION AND PWM CONTROL FOR ACTIVE POWER FILTERS Eyad KH Almaita, Ph.D. Western Michigan University, With the proliferation of nonlinear loads in the power system, harmonic pollution becomes a serious problem that affects the power quality in . of adaptive and sliding mode control []. A Radial-Basis Function Network (RBFN) is a branch of neural network which performs good to control the dynamics system. RBFN possesses great mapping ability and has a similar feature to the fuzzy system. The Radial-Basis Function Network can improve the control.

Three learning phases for radial-basis-function networks Friedhelm Schwenker*, Hans A. Kestler, Gu¨nther Palm Department of Neural Information Processing, University of Ulm, D Ulm, Germany Received 18 December ; accepted 18 December Abstract In this paper, learning algorithms for radial basis function (RBF) networks are discussed. In tro duction to Radial Basis F unction Net w orks Mark J L Orr Cen tre for Cognitiv e Science Univ ersit y of Edin burgh Buccleuc h Place Edin burgh EH L W Scotland April Abstract This do cumen tis anin tro duction to radial basis function RBF net w orks a t yp e of articial neural net w ork for application to problems sup ervised.


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Adaptive Noise Cancellation Using Recurrent Radial Basis Function Networks S. BILLNGS AND C. FUNG Department of Automatic Control and Systems University of SheTheld P. BoxMappin Street Sheffield 4DU Abstract eering Radial basis function neural network architectures are introduced for the nonlinear adaptive noise cancellation problem.

Active noise cancellation using recurrent radial basis function neural networks Conference Paper February with 43 Reads How we measure 'reads'. Vorobyov, S. A., and Cichocki, A., Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal, Digital Signal Processing11 () For the purpose of implementing real-time applications in nonlinear environments, an online self-enhanced fuzzy filter for solving adaptive noise cancellation is proposed.

The proposed online self-enhanced fuzzy filter is based on radial-basis-function networks and functionally is equivalent to the Takagi-Sugeno-Kang fuzzy by: 1.

“A Recurrent Neural Filter for Adaptive Noise Cancellation,”Proc. IASTED Int. Conf. on Artificial Intelligence and Applications, Innsbruck, Austria, pp. Google Scholar [8] P.A. Mastorocostas and J.B. Theocharis, “A Stable Learning Method for Block-Diagonal Recurrent Neural Networks: Application to the Analysis of Lung Sounds Author: Paris A.

Mastorocostas, Constantinos S. Hilas. This is similar to Active Noise Control Using Radial Basis Function Networks the performance achieved with the ideal controller, system achieves as good a performance as the system see Fig.

incorporating the ideal controller. dB 40 2O 10 0 -4O Frequency (Hz) Fig. by: In the field of mathematical modeling, a radial basis function network is an artificial neural network that uses radial basis functions as activation output of the network is a linear combination of radial basis functions of the inputs and neuron parameters.

Radial basis function networks have many uses, including function approximation, time series prediction. sive function estimation. INTRODUCTION In the past, radial basis function (RBF) network has been extensively studied due to its simple architecture and fast learning [2, 3, 6].

Typically, a RBF net describes its output to be a function of the inputs only. However, in some prac-tical problems such as nonlinear adaptive noise cancellationCited by: 4.

Keywords: Active Noise Control; Adaptive Nonlinear Control; DSP; Radial Basis Function Networks. 1 Introduction Basically, there are two approaches in control of acoustic noise: active and passive methods[1,2,4,5,12].

Active noise control (ANC), also referred to as active noise cancellation, has recently attracted much attention from engineers. The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS.

This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) by: 1.

not always the same activation function. In RBF networks, the hidden nodes (i.e., basis functions) have a very different purpose and operation to the output nodes. In RBF networks, the argument of each hidden unit activation function is the distance between the input and the “weights” (RBF centres), whereas in MLPs it.

Basis Function Optimization One major advantage of RBF networks is the possibility of determining suitable hidden unit/basis function parameters without having to perform a full non-linear optimization of the whole network. We shall now look at three ways of doing this: 1.

Fixed centres selected at random 2. Clustering based approaches 3. The full text of this article hosted at is unavailable due to technical difficulties. This paper presents a new adaptive critic controller to achieve precise position-tracking performance of induction motors using a radial basis function neural network (RBFNN).

The adaptive controller consists of an associative search network (ASN), an adaptive critic network (ACN), a feedback controller and a robust by: The paper presents an adaptive noise reduction system based on a microphone array.

An application of the presented system is the hands-free telephone in cars. In the paper the main ideas of the algorithms are outlined, and the results of the tests in realistic conditions are presented.

The system works under the assumption that noise in each of the microphone. The radial basis function approach introduces a set of N basis functions, one for each data point, which take the form φ(x −xp) where φ(⋅) is some non-linear function whose form will be discussed shortly.

Thus the pth such function depends on the distance x −xp, usually taken to be Euclidean, between x and xp. The output of the mapping. Radial Basis Function (RBF) networks are a classical fam-ily of algorithms for supervised learning. The goal of RBF is to approximate the target function through a linear com-bination of radial kernels, such as Gaussian (often inter-preted as a two-layer neural network).

Thus the output of an RBF network learning algorithm typically consists of aCited by: Adaptive filter noise cancellation systems using subband processing are developed and tested in this chapter.

Convergence and computational advantages are expected from using such a technique. Results obtained showed that; noise cancellation techniques using critically sampled filter banks have no convergence improvement, except for the case of Cited by: 3. Radial Basis Function Networks – Revisited David Lowe FIMA, Aston University 1 History and context The radial basis function (RBF) network is an adaptive network model introduced by Broomhead and Lowe [1] which was motivated by the mathematics of approximat- its role as a feed-forward adaptive network structure.

unknown function assumed. n 2 is related to n 1 via the highly nonlinear process shown previously; from the plots, it is hard to see if these two signals are correlated in any way.

The measured signal, m, is the sum of the original information signal, x, and the interference, n r, we do not know n only signals available to us are the noise signal, n 1, and the measured signal m. This is a script without useful comments. It deletes the workspace by the brute clearing header "close all;clear all;clc;", which is bad on onehand, because it removes debugger breakpoints also, and on the other hand it is nut user-friendly in a Reviews: 1.adaptive process, noise reduction can be accomplished with little risk of distorting the signal or increasing the output noise level.

In circumstances where adaptive noise cancelling is ap- plicable, levels of noise rejection are often attainable that would be difficult or impossible to achieve by direct filtering.Universal Approximation using Radial-Basis-Function Networks J. Park I. W. Sandberg Department ot' Electrical and Computer Engineering, Uniaersity of Texas at Austin, Austin, Texas 7g IISA 1 Introduction There have been several recent studies concerning feedforward net-Tolkr and the problem of approximating arbitra[, functionals of aFile Size: KB.