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.     

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 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     

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.     

An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems

Parameter estimation of chaotic systems is an important issue in the fields of computational mathematics and nonlinear science, which has gained increasing research and applications. In this paper, biogeography-based optimization (BBO), a new effective optimization algorithm based on the biogeography theory of the geographical distribution of biological organisms, is reasonably combined with differential evolution and simplex search to develop an effective hybrid algorithm for solving parameter estimation problem that is formulated as a multi-dimensional optimization problem. By suitably fusing several optimization methods with different searching mechanisms and features, the exploration and exploitation abilities of the hybrid algorithm can be enhanced and well balanced. Numerical simulation based on several typical chaotic systems and comparisons with some existing methods demonstrate the effectiveness of the proposed algorithm. In addition, the effects of population size and noise on the performances of the hybrid algorithm are investigated.     

An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm

A widely accepted magnetic resonance imaging (MRI) model states that the observed voxel intensity is a piecewise constant signal intensity function corresponding to the tissue spatial distribution, corrupted with multiplicative and additive noise. The multiplicative noise is assumed to be a smooth bias field, it is called intensity inhomogeneity (IIH) field. Our approach to IIH correction is based on the definition of an energy function that incorporates some smoothness constraints into the conventional classification error function of the IIH corrected image. The IIH field estimation algorithm is a gradient descent of this energy function relative to the IIH field. We call it adaptive field rule (AFR). We comment on the likeness of our approach to the self-organizing map (SOM) learning rule, on the basis of the neighboring function that controls the influence of the neighborhood on each voxel's IIH estimation. We discuss the convergence properties of the algorithm. Experimental results show that AFR compares well with state of the art algorithms. Moreover, the mean signal intensity corresponding to each class of tissue can be estimated from the image data applying the gradient descent of the proposed energy function relative to the intensity class means. We test several variations of this gradient descent approach, which embody diverse assumptions about available a priori information.     

An adaptive genetic algorithm for the time dependent inventory routing problem

In this paper we propose an adaptive genetic algorithm that produces good quality solutions to the time dependent inventory routing problem (TDIRP) in which inventory control and time dependent vehicle routing decisions for a set of retailers are made simultaneously over a specific planning horizon. This work is motivated by the effect of dynamic traffic conditions in an urban context and the resulting inventory and transportation costs. We provide a mixed integer programming formulation for TDIRP. Since finding the optimal solutions for TDIRP is a NP-hard problem, an adaptive genetic algorithm is applied. We develop new genetic representation and design suitable crossover and mutation operators for the improvement phase. We use adaptive genetic operator proposed by Yun and Gen (Fuzzy Optim Decis Mak 2(2):161–175, 2003) for the automatic setting of the genetic parameter values. The comparison of results shows the significance of the designed AGA and demonstrates the capability of reaching solutions within 0.5 % of the optimum on sets of test problems.     

An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle

This study presents an adaptive neuro-fuzzy sliding-mode-based genetic algorithm (ANFSGA) control system for a remotely operated vehicle (ROV) with four degrees of freedom (DOF)s. In many applications, ROVs will need to be capable of maneuvering to any given point, following object, and to be controllable from the surface. Therefore, an ANFSGA control system is introduced for tracking control of the ROV to achieve a high precision position control. Since the dynamic of ROVs are highly nonlinear and time varying, an ANFSGA control system is investigated according to direction-based genetic algorithm (GA) with the spirit of sliding mode control and adaptive neuro-fuzzy sliding mode (ANFS) based evolutionary procedure. In this way, on-line learning ability is employed to deal with the parametric uncertainty and disturbance by adjusting the ANFS inference parameters. In this proposed controller a GA control system is utilized to be the major controller, and stability can be indirectly insured by the concept of sliding mode control system without strict constraints and detailed system knowledge.     

基于遗传算法的伺服系统参数自整定

针对直流伺服电机的速度控制和位置跟踪问题,根据直流电机的数学模型,搭建了电机的三闭环控制系统模型;依据遗传算法对PID参数的寻优过程及方法,编写了基于遗传算法优化伺服参数的软件,并利用软件对电流环和速度环的控制参数做了寻优实验。实验证明:软件运行良好,基于遗传算法的PI参数整定方法可行,控制效果良好。     

An FPGA-based parallel architecture for on-line parameter estimation using the RLS identification algorithm

A parallel architecture for an on-line implementation of the recursive least squares (RLS) identification algorithm on a field programmable gate array (FPGA) is presented. The main shortcoming of this algorithm for on-line applications is its computational complexity. The matrix computation to update error covariance consumes most of the time. To improve the processing speed of the RLS architecture, a multi-stage matrix multiplication (MMM) algorithm was developed. In addition, a trace technique was used to reduce the computational burden on the proposed architecture. High throughput was achieved by employing a pipelined design. The scope of the architecture was explored by estimating the parameters of a servo position control system. No vendor dependent modules were used in this design. The RLS algorithm was mapped to a Xilinx FPGA Virtex-5 device. The entire architecture operates at a maximum frequency of 339.156 MHz. Compared to earlier work, the hardware utilization was substantially reduced. An application-specific integrated circuit (ASIC) design was implemented in 180 nm technology with the Cadence RTL compiler.