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PYRAMIDAL HYBRID APPROACH: WAVELET NETWORK WITH ols algorithm-BASED IMAGE CLASSIFICATION

INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2011年第1期9卷 111-130页

作者： Jemai, Olfa Zaied, Mourad Ben Amar, Chokri Alimi, Mohamed Adel Higher Inst Comp Sci Medenine 4119 Tunisia Natl Engn Sch Sfax ENIS Res Grp Intelligent Machines Sfax 3038 Tunisia

Taking advantage of both the scaling property of wavelets and the high learning ability of neural networks, wavelet networks have recently emerged as a powerful tool in many applications in the field of signal processing such as data compression, function approximation as well as image recognition and classification. A novel wavelet network-based method for image classification is presented in this paper. The method combines the Orthogonal Least Squares algorithm (ols) with the Pyramidal Beta Wavelet Network architecture (PBWN). First, the structure of the Pyramidal Beta Wavelet Network is proposed and the ols method is used to design it by presetting the widths of the hidden units in PBWN. Then, to enhance the performance of the obtained PBWN, a novel learning algorithm based on orthogonal least squares and frames theory is proposed, in which we use ols to select the hidden nodes. In the simulation part, the proposed method is employed to classify colour images. Comparisons with some typical wavelet networks are presented and discussed. Simulations also show that the PBWN-orthogonal least squares (PBWN-ols) algorithm, which combines PBWN with the ols algorithm, results in better performance for colour image classification.

关键词： Wavelet network beta function ols algorithm frames classification

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Non-linear system identification using particle swarm optimisation tuned radial basis function models

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第4期1卷 246-258页

作者： Chen, Sheng Hong, Xia Luk, Bing L. Harris, Chris J. Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England Univ Reading Sch Syst Engn Reading RG6 6AY Berks England City Univ Hong Kong Dept Mfg Engn & Engn Management Hong Kong Hong Kong Peoples R China

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network. model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

关键词： non-linear system orthogonal least squares algorithm ols algorithm leave-one-out cross validation tunable radial basis function network tunable RBF network particle swarm optimisation PSO

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A novel approach for high dimension 3D object representation using Multi-Mother Wavelet Network

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MULTIMEDIA TOols AND APPLICATIONS 2012年第1期59卷 7-24页

作者： Othmani, Mohamed Bellil, Wajdi Ben Amar, Chokri Alimi, Adel M. Univ Gafsa Higher Inst Appl Sci & Technol Gafsa 2112 Tunisia Univ Gafsa Fac Sci Gafsa 2112 Tunisia Univ Sfax Natl Sch Engineers ENIS REGIM Res Grp Intelligent Machines Sfax 3038 Tunisia

In this paper, we present a novel approach for 3D objects representation. Our idea is to prove that wavelet networks are capable for reconstruction and representing irregular 3D objects used in computer graphics. The major contribution consist to transform an input surface vertices into signals and to provide instantaneously an estimation of the output values for input values. To prove this, we will use a new structure of wavelet network founded on several mother wavelet families. This structure uses several mother wavelet, in order to maximize best wavelet selection probability. An algorithm to construct this structure is presented. First, Data is taken from 3D object. The vertices and their corresponding normal values of a 3D object are used to create a training set. To this stage, the training set can be expressed according to three functions, which interpolates all their vertices. Second we approximate each function using wavelet network. To achieve a better approximation, the network is trained several iterations to optimize wavelet selection for every mother. To guarantee a small error criterion, we adjust wavelet network parameters (weight, translation and dilation) by using an improved Orthogonal Least Squares method version. We consider our proposed approach on some 3D examples to prove that the new approach is able to approximate 3D objects with a good approximation ability.

关键词： 3D representation Multi-Mother Wavelet Networks ols algorithm Approximation Polywogs

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Multi-output regression using a locally regularised orthogonal least-squares algorithm

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 2002年第4期149卷 185-195页

作者： Chen, S Univ Southampton Dept Elect & Comp Sci Southampton SO17 1BJ Hants England

The paper considers data modelling using multi-output regression models. A locally regularised orthogonal least-squares (LRols) algorithm is proposed for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability of the multi-output orthogonal least-squares (ols) model selection to produce a parsimonious model with a good generalisation performance is greatly enhanced.

关键词： .hyperparameter regression model single-output over-fitting evidence procedure ols algorithm significant regressors

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Parameter estimation based on stacked regression and evolutionary algorithms

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IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS 1999年第5期146卷 406-414页

作者： Hong, X Billings, SA Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

A new parameter-estimation algorithm, which minimises the cross-validated prediction error for linear-in-the-parameter models, is proposed, based on stacked regression and an evolutionary algorithm. It is initially shown that cross-validation is very important for prediction in linear-in-the-parameter models using a criterion called the mean dispersion error (MDE). Stacked regression, which can be regarded as a sophisticated type of cross-validation, is then introduced based on an evolutionary algorithm, to produce a new parameter-estimation algorithm, which preserves the parsimony of a concise model structure that is determined using the forward orthogonal least-squares (ols) algorithm. The PRESS prediction errors ale used for cross-validation, and the sunspot and Canadian lynx time series are used to demonstrate the new algorithms.

关键词： stacked regression MDE evolutionary algorithms statistical analysis Simulation, modelling and identification Optimisation techniques parameter estimation sunspot time series linear-in-the-parameter models Canadian lynx time series ols algorithm evolutionary computation minimisation mean dispersion error Interpolation and function approximation (numerical analysis) cross-validation parsimony concise model structure PRESS prediction errors Other topics in statistics cross-validated prediction error minimisation least squares approximations forward orthogonal least-squares algorithm

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Computerized system for on-line treating with failure parameter using RBF neural network

Computerized system for on-line treating with failure parame...

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International Conference on Machine Learning and Cybernetics

作者： Xiao, ZH Zhang, ZX Huazhong Univ Sci & Technol Sch Hydropower & Digitilizat Engn Wuhan 430074 Peoples R China

ISBN: (纸本)0780378652

This paper investigates the application of a radial basis function (RBF) neural network (NN) to the treatment of failure parameter in the floodgate integrated automation system. For avoiding the drawbacks of arbitrary selecting centers, the RBF NN employs a learning procedure based on the orthogonal least squares (ols) method. This procedure chooses the radial basis function centers one by one in a rational way until an adequate network has been constructed. The ols algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output. The application results show that the RBF NN can be considered as a suitable technique for treating with failure parameter.

关键词： RBF neural networks ols algorithm centers

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HIERARCHICAL BAYESIAN CLASSIFIER BASED ON RBF-NN APPLIED IN THE ENVIRONMENT RECOGNITION FOR A MOBILE ROBOT

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IFAC Proceedings Volumes 2007年第15期40卷 535-541页

作者： F. Hendel N. Berrached Research laboratory in Intelligent Systems Electronic Department University of Sciences and Technology of Oran El M'nouar BP 1505 Oran Algeria

this paper presents hierarchical Bayesian classifier to recognize and rebuild the environment surrounding a mobile robot; it works with information coming from 16 sonar sensors supplying the distance measurements between the obstacles and the robot. This classifier has been implemented using Radial Basis Functions Neural Networks (RBF-NN) trained with Orthogonal Least Square algorithm “ols”. It sorts the environment into classes and operates a second processing to highlight objects. Additionally, instead to have only a binary decision such for example, a hard decision of impasse, the mobile robot decides for 90% of impasse situation. After the description of the classifier architecture and the training method, some simulation results are presented.

关键词： Mobile robot Environments recognition Radial Basis Functions Neural Networks (RBF-NN) ols algorithm Bayesian classifier

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