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Adaptive neural control for stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation

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IET CONTROL THEORY AND APPLICATIONS 2017年第14期11卷 2288-2298页

作者： Si, Wenjie Dong, Xunde Yang, Feifei South China Univ Technol Sch Automat Sci & Engn Ctr Control & Optimizat Guangzhou 510641 Guangdong Peoples R China

In this study, the adaptive tracking control is investigated for a class of stochastic pure-feedback non-linear time-delay systems with output constraint and asymmetric input saturation non-linearity. First, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, and the barrier Lyapunov function is designed to cope with the output constraints. Then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to address the effects of the unknown time-delay terms, and the neural network is employed to approximate the unknown non-linearities. At last, based on Lyapunov stability theory, a robust adaptive neural controller is proposed, which decreases the number of learning parameters and thus avoids the over-estimation problem. Under the designed neural controller, all the closed-loop signals are guaranteed to be 4-moment (or 2 moment) semi-globally uniformly ultimately bounded and the tracking error converges to a small neighbourhood of the origin for bounded initial conditions. Two simulation examples are presented to further illustrate the effectiveness of the designed method.

关键词： control system synthesis adaptive control neurocontrollers Lyapunov methods delay systems delays approximation theory convergence of numerical methods hyperbolic equations control nonlinearities feedback stochastic systems nonlinear control systems robust control robust adaptive neural controller stochastic pure-feedback nonlinear time-delay systems output constraint asymmetric input saturation adaptive tracking control input saturation nonlinearity Gaussian error function continuous differentiable asymmetric saturation model barrier Lyapunov function Lyapunov-Krasovskii functional hyperbolic tangent functions unknown time-delay terms neural network Lyapunov stability theory learning parameters closed-loop signals 4-moment semiglobally uniformly ultimately bounded 2-moment semiglobally uniformly ultimately bounded tracking error

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Heterogeneous Bare-Bones Particle Swarm Optimization for Dynamic Environments

Heterogeneous Bare-Bones Particle Swarm Optimization for Dyn...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Shen, Yuanxia Chen, Jian Zeng, Chuanhua Wei, Linna Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243002 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

Particle swarm optimization is an effective technique to track and find optimum in dynamic environments. In order to improve convergence accuracy of solutions, a heterogeneous bare-bones particle swarm optimization (HBPSO) is proposed in which several master swarms and a slaver swarm are employed to exploration search and exploitation search, respectively. When detecting environments change, a new strategy is used to update the position of particles for keeping swarm diversity. If the search areas of two swarms are overlapped, the worse swarm will be initialized. Experimental results on moving peaks benchmark (MPB) functions show that the proposed algorithm is effective and easy to implement.

关键词： Particle swarm optimization Dynamic environments learning parameters

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Ensemble Support Vector Machine Algorithm for Reliability Estimation of a Mining Machine

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2015年第8期31卷 1503-1516页

作者： Chatterjee, Snehamoy Dash, Ansuman Bandopadhyay, Sukumar Natl Inst Technol Dept Min Engn Rourkela 769008 Orissa India Univ Alaska Dept Min Engn Fairbanks AK 99701 USA

In this study, a support vector machine (SVM)-based ensemble model was developed for reliability forecasting. The hyperparameters of the SVM were selected by applying a genetic algorithm. Input variables of the SVM model were selected by maximizing the mean entropy value. The diverse members of the ensemble model were obtained by a k-means clustering algorithm, and one ensemble member was selected from each cluster by choosing the closest from the cluster center. The optimum cluster number was selected using the Davies-Bouldin index. The developed model was validated by a benchmark turbocharger data set. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted investigating a dumper operated at a coal mine in India. Time-to-failure historical data for the dumper were collected, and cumulative time to failure was calculated for reliability forecasting. Study results demonstrate that the developed model performs well with high accuracy (R-2 = 0.97) in the prediction of dumper failure, and a comparison with other methods demonstrates the superiority of the proposed ensemble SVM model. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： reliability forecasting ensemble modeling genetic algorithm k-means clustering learning parameters

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Parameter Estimation in Directed Evidential Networks from Evidential DataBases 6

Parameter Estimation in Directed Evidential Networks from Ev...

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6th International Conference on Soft Computing and Pattern Recognition (SoCPaR)

作者： Ben Hariz, Narjes Ben Yaghlane, Boutheina Inst Super Gest Tunis LARODEC Lab Tunis Tunisia

ISBN: (纸本)9781479959341

Evidential networks are considered as a powerful and flexible tools, commonly used for analyzing complex systems and handling different types of uncertainty in data. A crucial step to benefit from the reasoning process in these models is to quantify them. Thus, we address, in this paper, the issue of estimating parameters in evidential networks from evidential databases, by applying the maximum likelihood estimation generalized to the evidence theory framework.

关键词： Evidential Networks Evidential Databases learning parameters Belief Functions Maximum Likelihood

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Neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area

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COMPUTERS & GEOSCIENCES 2013年 52卷 246-257页

作者： Singh, Upendra K. Tiwari, R. K. Singh, S. B. Indian Sch Mines Dept Appl Geophys Dhanbad 826004 Bihar India Natl Geophys Res Inst Hyderabad 500006 Andhra Pradesh India

This paper presents the effects of several parameters on the artificial neural networks (ANN) inversion of vertical electrical sounding (VES) data. Sensitivity of ANN parameters was examined on the performance of adaptive backpropagation (ABP) and Levenberg-Marquardt algorithms (LMA) to test the robustness to noisy synthetic as well as field geophysical data and resolving capability of these methods for predicting the subsurface resistivity layers. We trained, tested and validated ANN using the synthetic VES data as input to the networks and layer parameters of the models as network output. ANN learning parameters are varied and corresponding observations are recorded. The sensitivity analysis of synthetic data and real model demonstrate that ANN algorithms applied in VES data inversion should be considered well not only in terms of accuracy but also in terms of high computational efforts. Also the analysis suggests that ANN model with its various controlling parameters are largely data dependent and hence no unique architecture can be designed for YES data analysis. ANN based methods are also applied to the actual YES field data obtained from the tectonically vital geothermal areas of Jammu and Kashmir, India. Analysis suggests that both the ABP and LMA are suitable methods for 1-D YES modeling. But the LMA method provides greater degree of robustness than the ABP in case of 2-D VES modeling. Comparison of the inversion results with known lithology correlates well and also reveals the additional significant feature of reconsolidated breccia of about 7.0 m thickness beneath the overburden in some cases like at sounding point RDC-5. We may therefore conclude that ANN based methods are significantly faster and efficient for detection of complex layered resistivity structures with a relatively greater degree of precision and resolution. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： ANN learning parameters VES Inversion Geothermal area

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Reliability estimation using a genetic algorithm-based artificial neural network: An application to a load-haul-dump machine

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第12期39卷 10943-10951页

作者： Chatterjee, Snehamoy Bandopadhyay, Sukumar Natl Inst Technol Dept Min Engn Rourkela 769008 Orissa India Univ Alaska Dept Min Engn Fairbanks AK 99701 USA

In this study, a neural network-based model for forecasting reliability was developed. A genetic algorithm was applied for selecting neural network parameters like learning rate (eta) and momentum (mu). The input variables of the neural network model were selected by maximizing the mean entropy value. The developed model was validated by applying two benchmark data sets. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted on a load-haul-dump (LHD) machine operated at a coal mine in Alaska, USA. Past time-to-failure data for the LHD machine were collected, and cumulative time-to-failure was calculated for reliability modeling. The results demonstrate that the developed model performs well with high accuracy (R-2 = 0.94) in the failure prediction of a LHD machine. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Systems reliability Variable selection Genetic algorithm Entropy learning parameters

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A New Approach to Construct Optimal Bow Tie Diagrams for Risk Analysis

A New Approach to Construct Optimal Bow Tie Diagrams for Ris...

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23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Badieddine, Ahmed Ben Amoi, Nahla Inst Super Gest Tunis LARODEC Le Baido 2000 Tunisia

ISBN: (纸本)9783642130243

Bow tie diagrams have become popular methods in risk analysis and safety management This tool describes graphically;in the same scheme, the whole scenario of an identified risk and its respective preventive and protective barriers The major problem with bow tie diagrams is that, they remain limited by then technical level and by then restriction to the graphical representation of different, scenarios without any consideration to the dynamic aspect. of real systems This paper overcomes this weakness by proposing a new Bayesian approach to construct bow ties horn teal data

关键词： Bow tie diagrams Risk analysis Bayesian networks learning structures learning parameters

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The smart gut: Tracking affective associative learning with measures of "liking", facial electromyography, and preferential looking

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learning AND MOTIVATION 2009年第1期40卷 74-93页

作者： Armel, K. Carrie Pulido, Carmen Wixted, John T. Chiba, Andrea A. Stanford Univ Sch Med Stanford Prevent Res Ctr Stanford CA 94305 USA San Diego State Univ San Diego CA 92182 USA Univ Calif San Diego San Diego CA 92103 USA Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA

We demonstrate here that initially neutral items can acquire specific value based on their associated outcomes, and that responses of physiological systems to such previously meaningless stimuli can rapidly reflect this associative history. Each participant participated in an associative learning task in which four neutral abstract pictures were each repeatedly paired with one of four foods that varied in valence and magnitude. Over the course of learning, participants' "liking" ratings of and preferences for each picture came to reflect the value of the food with which it was paired. The abstract pictures also elicited physiological responses characteristic of the foods with which they were paired, including changes in facial electromyography (EMG) and preferential looking. A logistic modeling procedure showed that learning parameters, such as the rate at which participants learned the values associated with the pictures, were similar across food outcomes of different value. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Human Conditioning Associative Pictures Visual stimuli Food Magnitude Valence Valence continuum Liking rating Preference Physiological Facial EMG Preferential looking Logistic learning parameters learning rate

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learning Bayesian network parameters from imperfect data: Enhancements to the EM algorithm

Learning Bayesian network parameters from imperfect data: En...

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Conference on Intelligent Computing - Theory and Applications V

作者： Hewawasam, Rohitha Premaratne, Kamal Univ Miami Dept Elect & Comp Engn Distributed Decis Environm Lab 1251 Mem Dr Coral Gables FL 33146 USA

ISBN: (纸本)9780819466822

The recent years have seen many developments in uncertainty reasoning taking place around Bayesian Networks (BNs). BNs allow fast and efficient probabilistic reasoning. One of the key issues that researchers have faced in using a BN is determining its parameters and structure for a given problem. Many techniques have been developed for learning BN parameters from a given dataset pertaining to a particular problem. Most of the methods developed for learning BN parameters from partially observed data have evolved around the Expectation-Maximization (EM) algorithm. In its original form, EM algorithm is a deterministic iterative two-step procedure that converges towards the maximum-likelihood (ML) estimates. The EM algorithm mainly focuses on learning BN parameters from imperfect data where some of the values are missing. However in many practical applications, partial observability results in a wider range of imperfections, e.g., uncertainties arising from incomplete, ambiguous, probabilistic, and belief theoretic data. Moreover, while convergence is to their ML estimates, the EM algorithm does not guarantee convergence to the underlying true parameters. In this paper, we propose an approach that enables one to learn BN parameters from a dataset containing a wider variety of imperfections. In addition, by introducing an early stopping criterion together with a new initialization method to the EM-algorithm, we show how the BN parameters could be learnt so that they are closer to the underlying true parameters than the converged ML estimated parameters.

关键词： Bayesian networks EM algorithm data uncertainties imperfect data learning parameters

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On supporting educational decision(s) for selectivity and/or development of software learning packages using artificial neural network modeling

On supporting educational decision(s) for selectivity and/or...

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11th World Multi-Conference on Systemics, Cybernetics and Informatics/13th International Conference on Information Systems Analysis and Synthesis

作者： Hassan, Hassan M. Al-Assaf, Mohammad ElShanty, Mohammad Computer Dept. Faculty of Teachers Hail University Saudi Arabia Arab Open University Hail Saudi Arabia

ISBN: (纸本)9781934272176

A novel approach for supporting optimality of educational decision(s) is presented. In other wards, having an optimal selection decision concerned with performance evaluation of some software learning packages, considered as a challenging educational issue. In practice, software learning packages are applied as computer instructional aids for some specific learning program(curriculum). So, these packages are presented as different alternatives subjected to optimal decisional choice of the best one. Otherwise, the decision for software development of some learning package(s) is highly recommended. This adopted decisional criterion to support educational systems is basically depends upon improvement in students' performances. Consequently, some learning parameters -that are candidates for measuring effectiveness and efficiency of such packages- are elected to support optimal educational decision(s). More specifically, two parameters that measure results after learning process convergence are suggested. The output learning level measuring educational achievement and the response time to fulfill some pre-assigned learning level. Artificial Neural Networks(ANNs) models are adopted to simulate realistically learning processes' performance as well as software learning packages' evaluation and testing. Finally, it is very interesting to declare that after running of suggested Artificial neural networks models, obtained results are exactly well supported by practical results obtained after educational field testing of one software learning packages presented herein as a case study.

关键词： educational technology decision support systems artificial neural networks modeling computer assisted instruction software learning packages learning parameters

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