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Nonlinear blind sensor fusion and identification

Nonlinear blind sensor fusion and identification

引用

International Conference on Information Fusion

作者： S.T. Roweis Department of Computer Science University of Toronto Toronto Canada

When several uncharacterized sensors measure the same unknown signal or image, we would like to simultaneously combine the measurements into an estimate of true source (fusion) and learn the properties of the individual sensors (identification). This paper presents a model in which sensors perform (time-invariant) linear filtering followed by pointwise nonlinear squashing with additive noise and shows how, given several such noisy nonlinear observations, it is possible to recover the true signal and also estimate the sensor parameters. The setup assumes that both the linear filtering and the nonlinear squashing are spatially (temporally) invariant, but does not make any prior assumptions (such as smoothness, sparsity or heavily tailed marginals) about the signal being recovered and thus is appropriate for a variety of source distributions, such as astronomical images, speech signals and hyperspectral satellite data which may violate one or more standard prior assumptions. An efficient estimation algorithm minimizes the sum of squared errors between the predicted sensor outputs and the sensor readings actually observed, using an efficient procedure, isomorphic to the backpropagation algorithm. The setup can be thought of as learning the weights and unknown common input for several one-layer neural networks given their outputs.

关键词： Sensor fusion Hyperspectral sensors Image sensors Extraterrestrial measurements Maximum likelihood detection Additive noise backpropagation algorithms Signal processing Parameter estimation Speech

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Neuro-sliding mode control for magnetic levitation systems

Neuro-sliding mode control for magnetic levitation systems

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Jiunshian Phuah Jianming Lu M. Yasser T. Yahagi Graduate School of Science and Technology Chiba University Chiba Japan

It is well known that sliding mode control (SMC) is capable of tackling systems with uncertainties. However, the discontinuous control signal causes the significant problem of chattering. Furthermore, thorough knowledge of the plant dynamics may be unknown or difficult to obtain, which makes it difficult to calculate the control law. A synergistic combination of neural network (NN) and SMC methodology is proposed. The network weights are adjusted using a modified online error backpropagation algorithm. Moreover, a new and simple approach is utilized to construct corrective controls of SMC to overcome the chattering problem. As a result, chattering is eliminated and the error performance of SMC is also improved. Experimental studies carried out on a magnetic levitation system are presented.

关键词： Control systems Magnetic levitation Sliding mode control Neural networks Magnetic variables control Actuators Nonlinear dynamical systems Nonlinear control systems Uncertainty backpropagation algorithms

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Synthesis of electrically thin and thick rectangular microstrip antennas with the use of adaptive neuro-fuzzy inference system

Synthesis of electrically thin and thick rectangular microst...

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： K. Guney N. Sarikaya Elektrik-Elektronik Mühendisliği Bölümü Mühendislik Fakültesi Erciyes Üniversitesi Kayseri Turkey Uçak Elektrik-Elektronik Bölümü Sivil Havacilik Yüksek Okulu Erciyes Üniversitesi Kayseri Turkey

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System identification using hierarchical fuzzy neural networks with stabel learnig algorithms

System identification using hierarchical fuzzy neural networ...

引用

IEEE Conference on Decision and Control

作者： Wen Yu M.A. Moreno-Armendariz Departamento de Control Automatico CINVESTAV Mexico Escuela de Ingeniería Dirección de Posgrado e Investigación Universidad de La Salle Mexico

Hierarchical fuzzy neural networks can use less rules to model nonlinear system with high accuracy. But the structure is very complex, the normal trainig for hierarchical fuzzy neural networks is difficult to realize. In this paper we use backpropagation-like approach to train the membership dunctions. The new learnig schemes employ a time-varying learning rate that is determined from input-output data and model structure. Stable learning algorithms for the premise and the consequence parts of fuzzy rules are proposed. The calculation of the learning rate does not need any prior information such as estimation of the modeling error bounds. The new algorithms are very simple, we can even train each sub-block of the hierarchical fuzzy neural networks independently.

关键词： System identification Fuzzy neural networks Fuzzy systems backpropagation algorithms Neural networks Nonlinear systems Fuzzy logic Noise robustness Multi-layer neural network Neurons

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Dead-zone Kalman filter algorithm for recurrent neural networks

Dead-zone Kalman filter algorithm for recurrent neural netwo...

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IEEE Conference on Decision and Control

作者： J. de Jesus Rubio Wen Yu Departamento de Control Automatico CINVESTAV Mexico

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustnees of Kalman filter algorithm dead-zone robust modification is applied to Kalman filter. Lyapunov method is used to prove that the Kalman filter training is stable.

关键词： Recurrent neural networks Neural networks backpropagation algorithms Noise robustness Stability analysis Convergence Nonlinear systems Lyapunov method Function approximation Filters

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Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study

Bayesian ANN classifier for ECG arrhythmia diagnostic system...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dayong Gao M. Madden D. Chambers G. Lyons Department of Information Technology National University of Ireland Galway Ireland

This paper outlines a system for detection of cardiac arrhythmias within ECG signals, based on a Bayesian artificial neural network (ANN) classifier. The Bayesian (or probabilistic) ANN classifier is built by the use of a logistic regression model and the backpropagation algorithm based on a Bayesian framework. Its performance for this task is evaluated by comparison with other classifiers including Naive Bayes, decision trees, logistic regression, and RBF networks. A paired t-test is employed in comparing classifiers to select the optimum model. The system is evaluated using noisy ECG data, to simulate a real-world environment. It is hoped that the system can be further developed and fine-tuned for practical application.

关键词： Bayesian methods Electrocardiography Artificial neural networks Logistics backpropagation algorithms Classification tree analysis Decision trees Regression tree analysis Radial basis function networks Working environment noise

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A collaborative filtering algorithm embedded BP network to ameliorate sparsity issue

A collaborative filtering algorithm embedded BP network to a...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Feng Zhang Hui-You Chang Software School Sun Yat-Sen University Guangzhou China Department of Computer Science Sun Yat-Sen University Guangzhou China

Collaborative filtering technologies are facing two major challenges: scalability and recommendation quality. Sparsity of source data sets is one major reason causing the poor recommendation quality. To reduce sparsity, we design a collaborative filtering algorithm who firstly selects users whose non-null ratings intersect the most as candidates of nearest neighbors, and then builds up backpropagation neural networks to predict values of the null ratings in the candidates. Experimental results show that this algorithm can increase the accuracy of nearest neighbors, resulting in improving recommendation quality of the recommendation system.

关键词： Collaboration Filtering algorithms Nearest neighbor searches backpropagation algorithms Recommender systems Collaborative software Scalability Neural networks Matrix decomposition Sun

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Traffic estimation and real time prediction using adhoc networks

Traffic estimation and real time prediction using adhoc netw...

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International Conference on Emerging Technologies, ICET

作者： F. Batool S.A. Khan Centre for Advanced Studies in Engineering

This paper presents the process of developing a multilayer feed forward neural network combined with a backpropagation algorithm for forecasting travel time and traffic congestion. Prediction of travel time and traffic congestion based on past and current traffic information is not straightforward due to among others, the high complexity and ill predictability of traffic process, incorrect observations and different data sources. However it appears that neural networks can be exhaustively used to solve these problems. The system is designed on top of a mesh based communication infrastructure for the mobile nodes to communicate. Communication network comprises of multiple networks, i.e. VHF, UHF. The mesh based communication approach enables easy deployment of the system in real world. OLSR routing protocol is used for establishing an ad hoc network for peer-to-peer-communication

关键词： Telecommunication traffic Multi-layer neural network Neural networks Mobile communication Peer to peer computing Feeds Feedforward neural networks backpropagation algorithms Communication networks Routing protocols

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Cosmetic defect classification found in ophthalmic lenses using artificial neural networks

Cosmetic defect classification found in ophthalmic lenses us...

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International Joint Conference on Neural Networks (IJCNN)

作者： M.I.M. Chacon D.A.V. Rodriguez J.M. Rivera A.J.S. Astudillo Chihuahua Institute of Technology DSP & Vision Lab Mexico

This paper presents the solution of a specific classification problem classification of cosmetic defect found in ophthalmic lenses - using artificial neural networks (ANN). In an ordinary industrial inspection process cosmetic defect classification involves a lot of subjectivity because the test depends on the appreciation of the cosmetic defect by a human inspector. Therefore, a machine classifier is of great help in order to reduce the effect of the subjectivity. The neural network classifier described in this paper is of relevance because it demonstrates the applicability of ANN to solve real world problems. The classifier also helped to discover a bias criteria during the inspection performed by human inspectors. Besides, the performance of the neural network impacts positively in the cost of incorrect decisions during cosmetic lens inspection. The paper shows the performance of several ANN classifiers designed. The best classifier turned out to be a multilayer perceptron trained with the backpropagation algorithm. This classifier has 94% of correct classification and solves the controversies between the production and quality departments due to subjectivity of the inspection.

关键词： Lenses Artificial neural networks Inspection Humans Neural networks Testing Costs Multilayer perceptrons backpropagation algorithms Production

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Min-max tuning of fuzzy models with uncertain data

Min-max tuning of fuzzy models with uncertain data

引用

Annual Conference of Industrial Electronics Society

作者： Mohit Kumar R. Stoll N. Stoll Center for Life Science and Automation Rostock Germany Institute of Occupational and Social Medicine Rostock Germany Institute of Automation Rostock Germany

This study deals with the robust tuning of a fuzzy model with uncertain data in a deterministic framework. We consider the on-line identification of an interpretable fuzzy model without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties. The tuning of the fuzzy model parameters is based on the robust (min-max) solution of a regularized least-squares constrained optimization problem. The simulation studies are carried out to show the better performance of our approach.

关键词： Fuzzy systems Uncertainty Noise robustness Automation Fuzzy set theory Filtering backpropagation algorithms Upper bound Statistical distributions Constraint optimization

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