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.     

基于改进粒子群算法的Hammerstein模型辨识

提出辨识非线性Hammerstein模型的新方法。将非线性系统的辨识问题转化为参数空间上的函数优化问题,采用粒子群算法获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出采用速度变异粒子群对整个参数空间进行搜索得到系统参数的最优估计。仿真结果验证了该方法的有效性。     

改进粒子群算法优化扩展卡尔曼滤波器电机转速估计

针对感应电机扩展卡尔曼滤波器转速估计中难以取得卡尔曼滤波器系统噪声矩阵和测量噪声矩阵最优值的问题, 提出了一种基于改进粒子群算法优化的扩展卡尔曼滤波器转速估计方法。算法通过融合遗传算法和粒子群算法的优点, 采用可调整的算法模型对粒子群算法进行改进, 将改进的粒子群算法对扩展卡尔曼滤波器中的系统噪声矩阵和测量噪声矩阵进行优化处理, 将优化后的卡尔曼滤波器应用于感应电机转速估计。仿真实验表明, 与试探法、标准粒子群算法及遗传算法比较, 改进粒子群算法优化的扩展卡尔曼滤波器能够有效提高转速估计的精度, 从而提高无速度传感器矢量控制系统的控制性能。     

A novel stability-based adaptive inertia weight for particle swarm optimization

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.     

基于数据驱动的感应电机多模型逆自适应解耦控制

提出一种基于数据驱动的感应电机多模型逆自适应解耦控制方法. 首先, 利用仿射聚类法(AP) 对电机系统的输入输出数据进行聚类, 再基于聚类结果和隶属度函数建立相应的神经网络多模型逆, 以实现解耦控制. 针对电机系统运行过程中电机参数变化问题, 采用粒子群优化算法(PSO) 在线调节子模型权值, 以改善逆模型失匹造成解耦控制性能下降的问题. 仿真实验表明, 所提出的方法能对电机的转速和磁链实现良好的解耦控制, 且对电机系统工况参数变化具有良好的自适应能力.

     

Parameter estimation of bilinear systems based on an adaptive particle swarm optimization

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to increase the convergence speed and accuracy of the basic particle swarm optimization to save tremendous computation time. An illustrative example for the modeling of bilinear systems is provided to confirm the validity, as compared with the Genetic Algorithm (GA), Linearly Decreasing Inertia Weight PSO (LDW-PSO), Nonlinear Inertia Weight PSO (NDW-PSO) and Dynamic Inertia Weight PSO (DIW-PSO) in terms of parameter accuracy and convergence speed. Second, APSO is also improved to detect and determine varying parameters. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a good promising particle swarm optimization algorithm for online parameter estimation.     

基于改进PSO的发酵过程同步串联混合建模

准确可靠的过程模型是实现发酵过程优化的基础和前提. 对于反应机理复杂的发酵过程,串联混合建模是一种相对有效的建模方法, 但现有方法需要利用插值所得的数据进行中间变量黑箱模型的构建, 较大程度地影响了所建混合模型的泛化性能. 为此,提出一种可将黑箱模型构建问题转化为动态模型参数辨识问题的同步串联混合建模方法, 从而避免了现有方法需利用插值数据来构建黑箱模型的不足; 通过引入多精英学习策略和惯性权重自适应调整策略, 构造了一种改进的粒子群优化(Particle swarm optimization, PSO)算法自适应多精英学习PSO (Adaptive multi-elite learning PSO, AMLPSO)算法,并采用该算法求取黑箱模型的参数; 借鉴均匀设计思想确定黑箱模型的结构. 利用诺西肽分批发酵过程实际生产数据进行实验研究, 结果验证了所提方法的有效性.     

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.     

PSO随机数参数设置的多目标定位方法研究

为了解决林业部门对森林防火安全监测系统中对多个声音目标的跟踪及定位问题,根据声音能量随距离衰减模型,提出了采用粒子群算法(PSO)的多目标定位与优化方法.通过利用极大似然法对声音强度模型的定位算法,采用惯性权重的粒子群算法,着重讨论了随机参数不同的设置方法对定位追踪精度性能的影响.通过仿真实验证明,粒子群算法中设置随机数参数为常数,可以有效提高目标定位精度,并减小搜索复杂度.     

基于粒子群算法的Logistic回归模型参数估计

针对Logistic回归模型中的参数估计计算复杂难题,提出一种基于粒子群优化算法（PSO）的估计方法。以最大似然准则作为粒子群优化算法的适应度函数,建立了Logistic回归模型中的参数估算模型。数值仿真分析表明,粒子群优化算法可以更精确地计算出相关参数。     

基于免疫粒子群算法的参数估计方法

针对污水生化反应模型参数估计问题,提出一种基于免疫粒子群算法的估计方法。该方法采用免疫算法保持粒子群的多样性,避免粒子群算法的过早收敛而降低寻优能力。利用估计的参数值对实验数据进行拟合,仿真结果表明,拟合误差率低于标准的粒子群和遗传算法,进一步提高了污水生化反应模型参数估计精度。     

Automatically extracting T–S fuzzy models using cooperative random learning particle swarm optimization

This paper proposes a methodology for automatically extracting T–S fuzzy models from data using particle swarm optimization (PSO). In the proposed method, the structures and parameters of the fuzzy models are encoded into a particle and evolve together so that the optimal structure and parameters can be achieved simultaneously. An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO. CRPSO employs several sub-swarms to search the space and the useful information is exchanged among them during the iteration process. Simulation results indicate that CRPSO outperforms the standard PSO algorithm, genetic algorithm (GA) and differential evolution (DE) on the functions optimization and benchmark modeling problems. Moreover, the proposed CRPSO-based method can extract accurate T–S fuzzy model with appropriate number of rules.     

Robust state estimator design for uncertain linear systems using optimization techniques

The estimation of state variables of dynamic systems in noisy environments has been an active research field in recent decades. In this way, Kalman filtering approach may not be robust in the presence of modeling uncertainties. So, several methods have been proposed to design robust estimators for the systems with uncertain parameters. In this paper, an optimized filter is proposed for this problem considering an uncertain discrete-time linear system. After converting the subject to an optimization problem, three algorithms are used for optimizing the state estimator parameters: particle swarm optimization (PSO) algorithm, modified genetic algorithm (MGA) and learning automata (LA). Experimental results show that, in comparison with the standard Kalman filter and some related researches, using the proposed optimization methods results in robust performance in the presence of uncertainties. However, MGA-based estimation method shows better performance in the range of uncertain parameter than other optimization methods.     

PSO算法在非线性回归模型参数估计中的应用

非线性回归模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对6种非线性回归模型的参数估计进行了验证。实验结果表明：粒子群优化算法是一种有效的参数估计方法。     

Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic

In this paper, we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behavior, which has been applied to different optimization problems obtaining good results. In this paper, we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. A comparison of the proposed method using type-2 fuzzy logic with the original PSO approach, and with PSO using type-1 fuzzy logic for dynamic parameter adaptation is presented.     

Particle swarm optimized multiple regression linear model for data classification

This paper presents a new data classification method based on particle swarm optimization (PSO) techniques. The paper discusses the building of a classifier model based on multiple regression linear approach. The coefficients of multiple regression linear models (MRLMs) are estimated using least square estimation technique and PSO techniques for percentage of correct classification performance comparisons. The mathematical models are developed for many real world datasets collected from UCI machine repository. The mathematical models give the user an insight into how the attributes are interrelated to predict the class membership. The proposed approach is illustrated on many real data sets for classification purposes. The comparison results on the illustrative examples show that the PSO based approach is superior to traditional least square approach in classifying multi-class data sets.     

Parameter optimization of sub-35 nm contact-hole fabrication using particle swarm optimization approach

The major research focus on integrated circuits (ICs) mainly deals with increasing circuit performance and functional complexity of circuit. The lithography process is the most critical step in the fabrication of nanostructure for integrated circuit manufacturing. The most important variable in the lithography process is the line-width or critical dimensions (CDs), which perhaps is one of the most direct impact variables on the device performance and speed. This study presents a hybrid approach combining Taguchi’s robust design, back-propagation neural network modeling technique and particle swarm optimization (PSO) for sub-35 nm contact-hole fabrication in the lithography process. The BP neural network is employed to model the functional relationship between the input parameters and target responses. Particle swarm optimization is adopted to optimize the parameter settings through the well-trained BP model, where each particle is assessed using fitness function. The proposed PSO algorithm applies the velocity updating and position updating formulas to the population composed of many particles such that better particles are generated. Compared with realistic fabricated and measured data, this approach can achieve the optimal parameter settings for minimized CDs or target CDs. Meanwhile, it reduces the CD variation through the design of experiment. The experimental results show that the proposed approach dealing with the process modeling and parameter optimization demonstrates its feasibility and effectiveness for sub-35 nm contact-hole fabrication.     

具有适应性突变和惯性权重的粒子群优化(PSO)算法及其在动态系统参数估计中的应用

An important problem in engineering is the unknown parameters estimation in nonlinear systems. In this paper, a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem. This work considers two new aspects, namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method. These mechanisms are employed to enhance global search ability and to increase accuracy. First, three well-known benchmark functions namely Griewank, Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum. The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA), in terms of parameter accuracy and convergence speed. It is confirmed that the proposed APSO is more successful than other aforementioned algorithms. Finally, the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.     

State estimation of nonlinear dynamic systems using weighted variance-based adaptive particle swarm optimization

New heuristic filters are proposed for state estimation of nonlinear dynamic systems based on particle swarm optimization (PSO) and differential evolution (DE). The methodology converts state estimation problem into dynamic optimization to find the best estimate recursively. In the proposed strategy the particle number is adaptively set based on the weighted variance of the particles. To have a filter with minimal parameter settings, PSO with exponential distribution (PSO-E) is selected in conjunction with jDE to self-adapt the other control parameters. The performance of the proposed adaptive evolutionary algorithms i.e. adaptive PSO-E, adaptive DE and adaptive jDE is studied through a comparative study on a suite of well-known uni- and multi-modal benchmark functions. The results indicate an improved performance of the adaptive algorithms relative to original simple versions. Further, the performance of the proposed heuristic filters generally called adaptive particle swarm filters (APSF) or adaptive differential evolution filters (ADEF) are evaluated using different linear (nonlinear)/Gaussian (non-Gaussian) test systems. Comparison of the results to those of the extended Kalman filter, unscented Kalman filter, and particle filter indicate that the adopted strategy fulfills the essential requirements of accuracy for nonlinear state estimation.     

基于粒子群优化的低阶时滞系统辨识

为了解决低阶时滞系统阶跃响应辨识问题,提出基于粒子群优化的参数估计方法.方法主要包括参数初值计算和参数估计两部分.首先,采用积分方程方法估计时滞系统参数初值,通过设置参数初值估计误差,得到系统参数取值范围.然后,为了减小由观测噪声引起的参数估计误差,采用粒子群优化算法优化模型参数.最后,通过仿真实验分别验证文中方法在不同噪声条件下辨识低阶时滞系统的性能.实验表明,文中方法具有良好的参数估计精度和较强的抗噪能力,可有效解决噪声条件下低阶时滞系统的阶跃响应辨识问题.