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Convergence analysis of a recursive prediction error method.

IFAC Proceedings Volumes 2012年第16期45卷 1496-1501页

作者： Soma Tayamon Torbjörn Wigren Uppsala University Department of Information Technology Division of System and Control Uppsala Sweden

A convergence analysis is performed for a recursive prediction error algorithm discretised using the midpoint integration method. Several conditions are formulated such that the stability of an associated differential equation can be tied to the local and global convergence properties of the algorithm. This shows that convergence to the true parameters is possible. The theoretical analysis of this paper is complemented by numerical example.

关键词： Nonlinear systems system identification prediction error methods recursive algorithms convergence analysis

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An Analytic Solution to the Optimal Design of Information Structure and Cooperative Control in Networked Systems

An Analytic Solution to the Optimal Design of Information St...

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IEEE Annual Conference on Decision and Control

作者： Zhihua Qu Marwan Simaan Department of EECS University of Central Florida

ISBN: (纸本)9781467320658

In this paper, the optimal design problem of both information structure and cooperative control is investigated for a collection of heterogeneous nonlinear systems. The concept of generalized passivity is presented to not only extend the concept of passivity to dynamical systems of any relative degree but also achieve the decoupling between designing a system-level self-feedback control law for any of the individual systems and designing a network-level distributed cooperative control as the command signal to the system-level control. The separation enables a new formulation in which both communication network topology and network-based cooperative control gains can be optimized in general. The performance indices in the formulation are chosen to minimize the cost of local communication network and to maximize convergence to consensus. Optimal gains in the cooperative controls can analytically be solved in terms of any information structure, and recursive algorithms of polynomial time are provided to find Pareto non-inferior solutions of information structures and the corresponding cooperative control gains.

关键词： information structure coordinated control Optimal design networks(communications) Passivity recursive algorithms performance indicators control law

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recursive Gauss-Newton Based Training Algorithm for Neural Network Modelling of an Unmanned Helicopter Dynamics

Recursive Gauss-Newton Based Training Algorithm for Neural N...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Syariful Syafiq Shamsudin XiaoQi Chen Department of Aeronautical Engineering Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Malaysia Mechanical Engineering Department University of Canterbury Christchurch 8041 New Zealand

ISBN: (纸本)9781467316439

This paper presents a recursive Gauss-Newton based training algorithm to model the dynamics of a small scale helicopter system using neural network modelling approach. It focuses on selection of optimized network for recursive algorithm that offers good generalization performance with the aid of the cross validation method proposed. The recursive method is then compared with off-line Levenberg-Marquardt (LM) training method to evaluate the generalization performance and adaptability of the model prediction. The results indicate that the recursive Gauss-Newton method gives slightly lower generalization performance compared to its off-line counterpart but adapts well to the dynamic changes that occur during flight. The proposed recursive algorithm was found effective in representing coupled helicopter dynamics with acceptable accuracy within the available computational timing constraint.

关键词： recursive algorithms recursive helicopter flight dynamics drone helicopters cross-validation Neural network OFF LINE Generalization Neural Networks (Computer) Modeling

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Online Updating Belief-Rule-Base Using the RIMER Approach

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2011年第6期41卷 1225-1243页

作者： Zhou, Zhi-Jie Hu, Chang-Hua Yang, Jian-Bo Xu, Dong-Ling Zhou, Dong-Hua High Tech Inst Xian Xian 710025 Peoples R China Tsinghua Univ TNList Dept Automat Beijing 100084 Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.

关键词： Belief-rule-base (BRB) evidential reasoning (ER) inference recursive algorithms uncertainty

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Online Updating With a Probability-Based Prediction Model Using Expectation Maximization Algorithm for Reliability Forecasting

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2011年第6期41卷 1268-1277页

作者： Hu, Chang-Hua Si, Xiao-Sheng Yang, Jian-Bo Zhou, Zhi-Jie Xian Inst Hitech Xian 710025 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based prediction model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive expectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data.

关键词： Decision analysis expectation maximization (EM) forecasting recursive algorithms uncertainty

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Non-linear minimum variance state-space-based estimation for discrete-time multi-channel systems

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IET SIGNAL PROCESSING 2011年第4期5卷 365-378页

作者： Grimble, M. J. Univ Strathclyde Ind Control Ctr Glasgow G1 1QE Lanark Scotland

A new state equation and non-linear operator-based approach to estimation is introduced for discrete-time multi-channel systems. This is a type of deconvolution or inferential estimation problem, where a signal enters a communications channel involving both non-linearities and transport delays. The measurements are corrupted by a coloured noise signal, which is correlated with the signal to be estimated both at the inputs and outputs of the channel. The communications channel may include either static or dynamic non-linearities represented in a general non-linear operator form. The optimal non-linear estimator is derived in terms of the state equations and non-linear operators that describe the system. The algorithm is relatively simple to derive and to implement in the form of a recursive algorithm. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. The results may be applied to the solution of channel equalisation problems in communications or fault detection problems in control applications.

关键词： equations of state deconvolution recursive algorithms Communication Research signals Colored noise noise signal

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A distributed origin-destination demand estimation approach for real-time traffic network management

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TRANSPORTATION PLANNING AND TECHNOLOGY 2011年第3期34卷 217-230页

作者： Etemadnia, Hamideh Abdelghany, Khaled So Methodist Univ Dept Civil & Environm Engn Dallas TX 75275 USA

This paper describes a distributed recursive heuristic approach for the origin-destination demand estimation problem for real-time traffic network management applications. The distributed nature of the heuristic enables its parallelization and hence reduces significantly its processing time. Furthermore, the heuristic reduces dependency on historical data that are typically used to map the observed link flows to their corresponding origin-destination pairs. In addition, the heuristic allows the incorporation of any available partial information on the demand distribution in the study area to improve the overall estimation accuracy. The heuristic is implemented following a hierarchal multi-threading mechanism. Dividing the study area into a set of subareas, the demand of every two adjacent subareas is merged in a separate thread. The merging operations continue until the demand for the entire study area is estimated. Experiments are conducted to examine the performance of the heuristic using hypothetical and real networks. The obtained results illustrate that the heuristic can achieve reasonable demand estimation accuracy while maintaining superiority in terms of processing time.

关键词： demand estimation recursive algorithms real-time traffic management

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Reliability Assessment of Lifeline Systems with Radial Topology

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2011年第2期26卷 111-128页

作者： Duenas-Osorio, Leonardo Rojo, Javier Rice Univ Dept Civil & Environm Engn Houston TX 77251 USA Rice Univ Dept Stat Houston TX USA

The increased susceptibility of lifeline systems to failure due to aging and external hazards requires efficient methods to quantify their reliability and related uncertainty. Monte Carlo simulation techniques for network-level reliability and uncertainty assessment usually require large computational experiments. Also, available analytical approaches apply mainly to simple network topologies, and are limited to providing average values, low order moments, or confidence bounds of reliability metrics. This study introduces a closed form technique to obtain the entire probability distribution of a reliability metric of customer service availability (CSA) for generic radial lifeline systems. A special case of this general formulation reduces to a simple sum of products equation, for which a recursive algorithm that exploits its structure is presented. This special-case algorithm computes the probability mass function (PMF) of CSA for systems with M elements in operations, relative to conventional operations, and opens the possibility of finding recursive algorithms for the general radial case. Parametric models that approximate the CSA metric are also explored and their errors quantified. The proposed radial topology reliability assessment tools and resulting probability distributions provide infrastructure owners with critical insights for informed operation and maintenance decision making under uncertainty.

关键词： topology reliability evaluation recursive algorithms Radial Probability distribution Surgery Forming techniques

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Range dependent clutter rejection using range-recursive space-time adaptive processing (STAP) algorithms

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SIGNAL PROCESSING 2010年第1期90卷 57-68页

作者： Beau, Sophie Marcos, Sylvie SUPELEC CNRS LSS F-91192 Gif Sur Yvette France

We here address the issue of ground clutter rejection for the detection of slowly moving targets in a non-side looking (NSL) array configuration airborne radar. The optimum space-time adaptive processing (STAP) filter needs the knowledge of the inverse of the space-time covariance matrix. In practice, it is unknown and has to be estimated. The most popular approximated method is the sample matrix inversion (SMI) method which consists in inverting the covariance matrix estimated by an average of the sample matrix over the secondary range cells. This estimator is unbiased in case of i.i.d. data. In an NSL configuration, the clutter power spectrum is range dependent and the data are consequently not i.i.d. We here present a solution to mitigate this range dependency of the data: the range recursive subspace-based algorithms. They are used in two architectures: a fully and a partially adaptive ones. Then a new range-recursive algorithm using Taylor series expansion is investigated. The performance of these algorithms are compared with that of the conventional STAP algorithms in term of SINR loss. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Airborne radar Space-time adaptive processing Range dependent clutter recursive algorithms

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Nonlinear black box identification of a Selective Catalytic Reduction system

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IFAC Proceedings Volumes 2011年第1期44卷 11845-11850页

作者： Soma Tayamon Darine Zambrano Torbjörn Wigren Bengt Carlsson Uppsala University Department of Information Technology Division of System and Control Uppsala Sweden

Abstract This paper discusses the identification of linear and non-linear black-box models for describing a diesel engine selective catalytic reduction (SCR) system. SCR aftertreatment systems form an important technology for reducing the NO x produced by diesel engines, and therefore good models are essential for the control of these systems. This paper compares a linear and a non-linear model for identification of the system. The output signals of the SCR were generated from 4 measured input signals, using a simulated 18 state model. The experiments with a recursive prediction error method, RPEM, with only 2 states show that the system can be accurately approximated with a much simpler model. The RPEM estimates 16 unknown parameters while the linear model uses 9 parameters. The results were compared based on the model fit and it was clear from the validation data set that the non-linear model gives better results and captures more of the system dynamics as compared to the linear model. A comparison of the RPEM using the midpoint integration method and the Euler method for discretisation was also made for the models. The results clearly show that the more accurate discretisation algorithm results in a better model fit.

关键词： Non-linear systems prediction error methods recursive algorithms selective catalytic reduction systems diesel engines system identification

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