Stochastic sampling algorithms for state estimation of jump Markovlinear systems

Jump Markov linear systems are linear systems whose parameters evolve with time according to a finite-state Markov chain. Given a set of observations, our aim is to estimate the states of the finite-state Markov chain and the continuous (in space) states of the linear system. The computational cost in computing conditional mean or maximum a posteriori (MAP) state estimates of the Markov chain or the state of the jump Markov linear system grows exponentially in the number of observations. We present three globally convergent algorithms based on stochastic sampling methods for state estimation of jump Markov linear systems. The cost per iteration is linear in the data length. The first proposed algorithm is a data augmentation (DA) scheme that yields conditional mean state estimates. The second proposed scheme is a stochastic annealing (SA) version of DA that computes the joint MAP sequence estimate of the finite and continuous states. Finally, a Metropolis-Hastings DA scheme based on SA is designed to yield the MAP estimate of the finite-state Markov chain. Convergence results of the three above-mentioned stochastic algorithms are obtained. Computer simulations are carried out to evaluate the performances of the proposed algorithms. The problem of estimating a sparse signal developing from a neutron sensor based on a set of noisy data from a neutron sensor and the problem of narrow-band interference suppression in spread spectrum code-division multiple-access (CDMA) systems are considered     

遗传算法在系统可靠性优化中的应用

研究性等价、体积和重量约束条件下，多级串联系统和桥式网络系统可靠性优化问题.使用遗传算法对该问题进行求解，利用基于排名的选择方法和最优保存策略，改善了遗传算法的收敛性能。计算机仿真实验结果表明，用遗传算法求解该问题是有效的。     

Testing real-time systems using genetic algorithms

The development of real-time systems is an essential industrial activity whose importance is increasing. The most important analytical method to assure the quality of real-time systems is dynamic testing. Testing is the only method which examines the actual run-time behaviour of real-time software, based on an execution in the real application environment. Dynamic aspects like the duration of computations, the memory actually needed, or the synchronization of parallel processes are of major importance for the correct function of real-time systems and have to be tested. A comprehensive investigation of existing software test methods shows that they mostly concentrate on testing for functional correctness. They are not suited for an examination of temporal correctness which is essential to real-time systems. Very small systems show a wide range of different execution times. Therefore, existing test procedures must be supplemented by new methods, which concentrate on determining whether the system violates its specified timing constraints. In general, this means that outputs are produced too early or their computation takes too long. The task of the tester is to find the inputs with the longest or shortest execution times to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, genetic algorithms can be used to find the inputs with the longest or shortest execution times automatically. The fitness function is the execution time measured in processor cycles. Experiments using genetic algorithms on a number of programs with up to 1511 LOC and 843 integer input parameters have successfully identified new longer and shorter paths than had been found using random testing or systematic testing. Genetic algorithms are able therefore to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.     

Application of state estimation to target tracking

In this paper, we present a survey of problems and solutions in the area of target tracking. The discussion includes design tradeoffs, performance evaluation, and current issues.     

Parameter estimation of systems subject to random state changes

Parameter estimation for a linear regression model subject to abrupt random state changes is formulated as an optimization problem. An identification algorithm comprising state regime classification and parameter identification is developed. The samples of the system are first partitioned into different groups called clusters, corresponding to different states. The standard linear-least-square method is then used to identify the parameters. The cluster control is a matrix of predetermined rank and can be computed by the singular-value-decomposition algorithm. Two iterative algorithms that ensure the decrease of the objective function are then proposed. An example is given to show the effectiveness of the method     

Active state estimation of nonlinear systems

In this paper, state observers for control systems with nonlinear outputs are studied. For such systems, the observability does not only depend on the initial conditions, but also on the exciting control used. Thus, for such systems, design of active control is an integral part of the design for state observers. Here some sufficient conditions are given for the convergence of an observer. It is also discussed, via a camera example, how to actively excite a system in order to improve the observability.     

Frequency estimation of undamped exponential signals using genetic algorithms

In this paper, we consider the problem of frequency estimation of undamped superimposed exponential signals model. We propose two iterative techniques of frequency estimation using genetic algorithms. The proposed methods use an elitism based generational genetic algorithm for obtaining the least squares and the approximate least squares estimates. In the simulation studies, it is observed that the proposed methods give nearly efficient estimates, having mean square error almost attaining the corresponding Cramér-Rao lower bound. The proposed methods significantly do not depend on the initial guess values otherwise required for other iterative methods of frequency estimation. It is also observed that the proposed methods have fairly high breakdown point with respect to different types of outliers present in the data. Outlier robustness and accuracy of the proposed methods are compared with the classical approaches for this problem.     

动态系统运动状态最优化估计研究

动态系统运动状态最优化估计是运动分析领域的一个重要研究内容.以卡尔曼滤波器为研究基点,进一步研究分析了动态系统运动状态最优化估计方法.工作主要从两个方面展开.首先建立动态系统运动状态相适应的数学模型,通过数学推导分析研究了卡尔曼滤波器、H-滤波器和扩展卡尔曼滤波器的设计原理和应用范围.然后就车辆导航系统和永磁同步电机系统作了仿真实验.仿真结果表明了理论分析的正确性.     

Exploring very large state spaces using genetic algorithms

We present a novel framework for exploring very large state spaces of concurrent reactive systems. Our framework exploits application-independent heuristics using genetic algorithms to guide a state-space search toward error states. We have implemented this framework in conjunction with VeriSoft, a tool for exploring the state spaces of software applications composed of several concurrent processes executing arbitrary code. We present experimental results obtained with several examples of programs, including a C implementation of a public-key authentication protocol. We discuss heuristics and properties of state spaces that help a genetic search detect deadlocks and assertion violations. For finding errors in very large state spaces, our experiments show that a genetic search using simple heuristics can significantly outperform random and systematic searches.     

Dynamic state and parameter estimation applied to neuromorphic systems

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.     

Using genetic algorithms to generate test sequences for complex timed systems

The generation of test data for state-based specifications is a computationally expensive process. This problem is magnified if we consider that time constraints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, supported by tools, that addresses this issue by representing the test data generation problem as an optimization problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be easily adapted to be used with other evolutionary search techniques.     

Application of genetic algorithms to optimization of neuronet recognition devices

The possibilities of applying genetic algorithms to optimization of the structure of neural networks that solve problems of recognition of handwritten and printed symbols and words are considered. The results of an experimental study are given. The experiments performed demonstrate an increase in the efficiency of neural networks after optimization. Ways of improving the results obtained are discussed. These results were partially obtained due to grant U4M000 of the Soros International Scientific Fund and also due to the“Neurocomputer” project (1992-994) of the State Committee of NAS of Ukraine on Science and Engineering. Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 23–32, September–October, 1999.     

基于进化个体模糊适应值估计的交互式遗传算法

采用大规模种群进化优化策略,根据用户评价时间和单一数值适应值估计个体模糊适应值;根据个体表现型属性和参照个体模糊适应值宽度计算个体表现型相似度;利用个体表现型相似度对种群聚类并估计未评价个体的模糊适应值;基于个体模糊适应值和表现型相似性构造个体选择适应值,实现个体相似性选择.将所提出方法应用于室内挂钟进化设计,并与已有典型方法进行比较.结果表明,所提出方法在提高优化质量、减轻用户疲劳、提高搜索效率等方面均具有优越性.     

Moving-horizon partition-based state estimation of large-scale systems

This paper presents three novel moving-horizon estimation (MHE) methods for discrete-time partitioned linear systems, i.e., systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states and noise in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, proper tuning of the design parameters, i.e., the penalties on the states at the beginning of the estimation horizon, guarantees convergence of the estimation error to zero. Numerical simulations demonstrate the viability of the approach.     

Nonlinear bounded-error state estimation of continuous-time systems

This paper presents a first study on the application of interval analysis and consistency techniques to state estimation of continuous-time systems described by nonlinear ordinary differential equations. The approach is presented in a bounded-error context and the resulting methodology is illustrated by an example.     

Reduced-order modeling and state estimation applied to gas distribution systems

A reduced-order model for complex multifeed gas networks has been developed which is based on an aggregation procedure. The reduced model is then used with an estimation scheme to determine the pressure distribution in the network from a limited number of measurements. The results obtained show that the model yields good steady-state data over a range of operating conditions, and hence a robust estimation scheme has been developed.     

An alternative approach to state estimation in linear time-invariant systems

An alternative approach to state estimation problem in linear, time-invariant dynamic systems is presented in this paper. The approach developed first identifies the initial state of the system by using a proportional plus integral parameter identification method. The Lyapunov design technique is used to guarantee the asymptotic convergence of the initial state identifier. A state estimator is then constructed to operate in series with the initial state identifier. The estimator generates an estimate of the unobserved part of the system state. Simulation studies have shown that satisfactory state estimation can be achieved in the presence of measurement or disturbance noise. An example problem is considered to demonstrate the response characteristics of the estimator-identifier combination.     

小生境遗传模糊PID控制在皮带秤中的应用

采用模糊PID方法控制流量恒定,并结合遗传算法优化PID参数.引入小生境技术,克服了传统遗传算法早熟收敛和后期收敛速度慢以及收敛效率低的缺点,同时对遗传算子也进行了改进,获得理想的全局寻优效果.     

Cooperative state estimation of multi-agent systems subject to bounded external disturbances

This paper deals with the problem of cooperative state estimation of general linear multi-agent systems subject to heterogeneous bounded external disturbances. This problem specifies the objective that each agent estimates its own state by using only relative information from its neighbours. Because of the existence of external disturbances, the problem is challenging especially when stringent exact estimation performance is desired. To this end, two cooperative estimation protocols are proposed, including the discontinuous nonlinear protocol for exact state estimation and the continuous nonlinear protocol for estimation with bounded errors. The overall network stability and convergence properties are analysed using the Lyapunov function method. A simulation example has also been used to demonstrate the effectiveness of the proposed results.     

Modeling individual multifactor estimation using GMDH elements and genetic algorithms

A method is proposed for comparative structural-parametric identification of a model of individual multifactor estimation. An experimental solution to this problem is obtained using evolutionary methods. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 151–159, January–February 2007.