基于自适应遗传算法的多项式模型结构与参数的一体化辨识

基于自适应遗传算法,提出一种多项式模型结构与参数的一体化辨识方法.针对组合非线性系统,首先将选定的候选项原始序列与输出序列进行相关度评估,根据其大小排列进行遗传算法染色体结构的自适应编码;在迭代辨识充分后,再次计算候选项贡献序列与由该项造成的模型损失序列间的相关度,剔除相关度较小的项,调整模型结构;如此循环迭代,在完成参数辨识的同时最终确认模型结构.仿真实例验证了算法的有效性.     

同参数估计对偶的自适应控制算法

本文把线性和非线性系统统一处理。从自适应控制算法与参数估计算法的对偶性出发,提出了自适应控制算法的一种统一格式。这种格式算法简单,并在一定的条件下,能使控制误差一致的足够小。     

An algorithm for steady-state system optimization and parameter estimation

In this paper an algorithm is described which uses a steady-state mode! to determine the optimum operating point of a process. The model, which is not required to be an accurate representation of the real process, contains parameters to be estimated and the algorithm involves an iterative procedure between the two problems of system optimization and parameter estimation. Lagrangian analysis is employed to account for the interaction between the two problems, resulting in a procedure which may be regarded as a modified two-step approach in which the optimization objective index includes an extra term. The extra term contains a comparison between model and real process output derivatives and ensures that the optimal steady-state operating condition is achieved in spite of model inaccuracies.

The algorithm is shown to perform satisfactorily in a digital simulation study concerned with determining food flow rate and temperature controller set points to maximize the net rate of return from an exothermic chemical reactor using a simplified non-linear model for system optimization and parameter estimation. The simulation is employed to investigate the convergence properties of the algorithm and to study the effects of measurement errors.     

非线性系统模型参数估计的算法模型

针对非线性系统模型的多样性,提出了适用于多种非线性模型的基于粒子群优化算法的参数估计方法。计算结果表明,粒子群优化算法是非线性系统模型参数估计的有效工具。     

An extension to the modified two-step algorithm for steady-state system optimization and parameter estimation

This paper presents an extension to the modified two-step algorithm for determining the optimum steady-state operating condition of a system. The new version of the algorithm gives a faster convergent rate and ensures that the optimal condition is achieved in more general cases where system inequality constraints involving system outputs occur. The performance of the algorithm under noisy measurements is examined by simulation. Simple filter techniques are employed to attenuate errors in process measurements.     

不确定Chua电路系统的固定时间自适应参数估计

针对具有未知参数的不确定Chua电路系统, 本文提出了一种基于Volterra积分算子的固定时间自适应参数 估计算法. 在仅有输出信号的已知的情况下, 该算法能够保证参数的估计值在一个不依赖于初始误差的固定时间内 收敛到参数的真实值. 通过在Volterra积分算子中巧妙的选取核函数, 使其满足在τ = 0和τ = t时核函数及其导数为 零, 从而能够有效消除系统初始值的影响, 同时避免了对系统输出导数的计算. 最后, 仿真结果验证了所提算法的有 效性.     

Maximum likelihood parameter estimation algorithm for controlled autoregressive autoregressive models

A maximum likelihood parameter estimation algorithm is derived for controlled autoregressive autoregressive (CARAR) models based on the maximum likelihood principle. In this derivation, we use an estimated noise transfer function to filter the input–output data. The simulation results show that the proposed estimation algorithm can effectively estimate the parameters of such class of CARAR systems and give more accurate parameter estimates than the recursive generalized least-squares algorithm.     

An adaptive parallel genetic algorithm system for i‐Computing environment

Many real‐world optimization problems in the scientific and engineering fields can be solved by genetic algorithms (GAs) but it still requires a long execution time for complex problems. At the same time, there are many under‐utilized workstations on the Internet. In this paper, we present a self‐adaptive parallel GA system named APGAIN, which utilizes the spare power of the heterogeneous workstations on the Internet to solve complex optimization problems. In order to maintain a balance between exploitation and exploration, we have devised a novel probabilistic rule‐driven adaptive model (PRDAM) to adapt the GA parameters automatically. APGAIN is implemented on an Internet Computing system called DJM. In the implementation, we discover that DJM's original load balancing strategy is insufficient. Hence the strategy is extended with the job migration capability. The performance of the system is evaluated by solving the traveling salesman problem with data from a public database. Copyright © 2003 John Wiley & Sons, Ltd.     

An adaptive state estimation solution to the maneuvering target problem

A new approach to tracking a maneuvering target is presented. Modeling the randomly varying target as a semi-Markov process and then incorporating the statistics into the design of an adaptive state estimator produced an estimation scheme that greatly reduced the large bias errors that usually appear when a target makes an unexpected, large-scale maneuver.     

An optimal volume ellipsoid algorithm for parameter set estimation

A recursive ellipsoid algorithm is derived for estimating the parameter set of a single-input single-output linear time-invariant system with bounded noise. The algorithm's objective is to seek the minimal volume ellipsoid bounding the feasible parameter set. Cast in a recursive framework, where a minimal volume ellipsoid results at each recursion, the algorithm extends a result due to Khachian (see Aspvall and Stone, 1980) in which a technique was developed to solve a class of linear programming problems. This extension and application to the parameter set estimation problem have intuitive geometric appeal and are easy to implement. Comparisons are made to the optimal bounding ellipsoid algorithm of Fogel and Huang (1982), and the results are demonstrated through computer simulations     

自适应不规则形状扩展目标跟踪算法

针对具有不规则形状的扩展目标跟踪问题,在目标先验形状未知和运动过程中目标形状发生突变的情况下,提出基于星凸形随机超曲面模型的自适应轮廓扩展目标跟踪算法.首先,研究星凸形扩展目标形状的径向函数傅里叶系数自适应设定问题,基于中心轮廓距离提出不规则形状的自适应方法.其次,针对扩展目标在运动过程中可能发生的形状突变问题,结合滑动窗口构造检测统计量对目标形变进行实时检测,并提出自适应轮廓快速逼近算法对形状突变的扩展目标进行实时跟踪.然后,提出一种拟Jaccard距离作为形状估计质量的评价指标.最后,仿真实验验证了本文所提算法的有效性.     

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.     

An algorithm for nonlinear system parameter identification

An algorithm for system parameter identification will be presented in this paper. The method is applicable to linear and nonlinear systems with known structures. It is applied in this paper to systems in which both system and measurement noises can be neglected. The algorithm requires less time per iteration and less computer storage than the quasilinearization method. A shaft position control system with multiple nonlinearities will be used to illustrate the method.     

An adaptive hybrid genetic algorithm for the three-matching problem

This paper presents a hybrid genetic algorithm (GA) with an adaptive application of genetic operators for solving the 3-matching problem (3MP), an NP-complete graph problem. In the 3MP, we search for the partition of a point set into minimal total cost triplets, where the cost of a triplet is the Euclidean length of the minimal spanning tree of the three points. The problem is a special case of grouping and facility location problems. One common problem with GA applied to hard combinatorial optimization, like the 3MP, is to incorporate problem-dependent local search operators into the GA efficiently in order to find high-quality solutions. Small instances of the problem can be solved exactly, but for large problems, we use local optimization. We introduce several general heuristic crossover and local hill-climbing operators, and apply adaptation to choose among them. Our GA combines these operators to form an effective problem solver. It is hybridized as it incorporates local search heuristics, and it is adaptive as the individual recombination/improvement operators are fired according to their online performance. Test results show that this approach gives approximately the same or even slightly better results than our previous, fine tuned GA without adaptation. It is better than a grouping GA for the partitioning considered. The adaptive combination of operators eliminates a large set of parameters, making the method more robust, and it presents a convenient way to build a hybrid problem solver     

Newton-like algorithm for integrated system optimization and parameter estimation technique

The problem of obtaining the correct optimal steady-state operation of an industrial process, despite the deficiencies in its available mathematical model, is addressed in the paper. The control method considered is the integrated system optimization and parameter estimation (ISOPE) technique, and a new algorithm, of Newton-like type, is presented in the paper for this technique. The algorithm is derived for the augmented version of the ISOPE technique and hence it is also applicable for non-convex problems. The algorithm does not require more measurement information than the previous simple relaxation-type algorithms. It is convergent in one iteration for the case of a linear process and quadratic performance function. A local convergence analysis is provided for the general non-linear case. Simulation results are also given, and comparison with the relaxation type algorithm is presented, indicating the superiority of the new approach for the examples considered     

On an algorithm for combined system optimisation and parameter estimation

The paper is concerned with the determination of optimum steady-state operation of industrial plant where the optimisation is performed using a mathematical model with parameters whose values are estimated by comparing model and real plant measurements. The two associated problems of system optimisation and model parameter estimation are discussed and an algorithm is examined whose purpose is to accomplish the correct steady-state optimum operating condition on the real plant in spite of inaccuracies in the structure of the mathematical model. The aim of the paper is to investigate the performance of the algorithm which is accomplished through a theoretical analysis of its application to a linear process, where the optimisation is performed using a quadratic performance index and a mathematical model of incorrect structure. Particular emphasis is given to the stability and convergence properties of the algorithm and to the effect of real process measurement errors. Simulation results are also presented illustrating the effectiveness of the technique when applied to nonlinear optimisation problems including a study concerned with determining optimum controller set points to maximise the net rate of return from a chemical reactor plant.     

Fast parametric imaging algorithm for dual-input biomedical system parameter estimation

Medical parametric imaging with dynamic positron emission tomography (PET) plays an increasingly potential role in modern biomedical research and clinical diagnosis. The key issue in parametric imaging is to estimate parameters based on sampled data at the pixel-by-pixel level from certain dynamic processes described by valid mathematical models. Classic nonlinear least squares (NLS) algorithm requires a "good" initial guess and the computational time-complexity is high, which is impractical for image-wide parameter estimation. Although a variety of fast parametric imaging techniques have been developed, most of them focus on single input systems, which do not provide an optimal solution for dual-input biomedical system parameter estimation, which is the case of liver metabolism. In this study, a dual-input-generalized linear least squares (D-I-GLLS) algorithm was proposed to identify the model parameters including the parameter in the dual-input function. Monte Carlo simulation was conducted to examine this novel fast algorithm. The results of the quantitative analysis suggested that the proposed technique could provide comparable reliability of the parameter estimation with NLS fitting and accurately identify the parameter in the dual-input function. This method may be potentially applicable to other dual-input biomedical system parameter estimation as well.     

Novel techniques in parameter estimation for fractional dynamical models arising from biological systems

In recent years, both parameter estimation and fractional calculus have attracted a considerable interest. Parameter estimation of the fractional dynamical models is a new topic. In this paper, we consider novel techniques for parameter estimation of fractional nonlinear dynamical models in systems biology. First, a computationally effective fractional Predictor-Corrector method is proposed for simulating fractional complex dynamical models. Second, we convert the parameter estimation of fractional complex dynamical models into a minimization problem of the unknown parameters. Third, a modified hybrid simplex search (MHSS) and a particle swarm optimization (PSO) is proposed. Finally, these techniques are applied to a dynamical model of competence induction in a cell with measurement error and noisy data. Some numerical results are given that demonstrate the effectiveness of the theoretical analysis.     

A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation

This paper introduces a novel hybrid adaptive cuckoo search (HACS) algorithm to establish the parameters of chaotic systems. In order to balance and enhance the accuracy and convergence rate of the basic cuckoo search (CS) algorithm, the adaptive parameters adjusting operation is presented to tune the parameters properly. Besides, the exploitation capability of the CS algorithm is enhanced a lot by integrating the orthogonal design strategy. The functionality of the HACS algorithm is tested through the Lorenz system under the noise-free and noise-corrupted conditions, respectively. The numerical results demonstrate that the algorithm can estimate parameters efficiently and accurately, and the capability of noise immunity is also powerful. Compared with the basic CS algorithm, genetic algorithm, and particle swarm optimization algorithm, the HACS algorithm is energy efficient and superior.