Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.     

A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm

Targeting the non-linear dynamic characteristics of roller bearing faulty signals, a fault feature extraction method based on hierarchical entropy (HE) is proposed in this paper. SampEns of 8 hierarchical decomposition nodes (e.g. HE at scale 4) are calculated to serve as fault feature vectors, which takes into account not only the low frequency components but also high frequency components of the bearing vibration signals. HE can extract more faulty information than multi-scale entropy (MSE) which considers only the low frequency components. After extracting HE as feature vectors, a multi-class support vector machine (SVM) is trained to achieve a prediction model by using particle swarm optimization (PSO) to seek the optimal parameters of SVM, and then ten different bearing conditions are identified through the obtained SVM model. The experimental results indicate that HE can depict the characteristics of the bearing vibration signal more accurately and more completely than MSE, and the proposed approach based on HE can identify various bearing conditions effectively and accurately and is superior to that based on MSE.     

A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.     

Fractional order model identification using the sinusoidal input

An output error optimization approach for identification of parsimonious fractional order models using multi-frequency sinusoids as input is proposed. The algorithm simultaneously estimates orders, parameters and the delay of simple models with fractional orders using the Gauss–Newton optimization approach. Optimization-based methods for fractional order model identification require evaluation of the sensitivity functions which include the logarithmic derivatives of the input signal. In the existing literature, central difference or similar methods are used to numerically calculate the Jacobian matrix due to difficulties with numerical simulation of the logarithmic derivatives. We assume deterministic input signals and provide analytical expressions for the logarithmic derivatives of single and multiple frequency sinusoids. Relevant mathematical derivations are presented and the analytical expressions are used to evaluate the Jacobian. Effects of noise to signal ratio, input frequency and sampling intervals are studied in simulation to demonstrate the efficacy of the method. Convergence and robustness of the method is also studied. In theory, the approach is applicable for models with large set of parameters; however, convergence of the optimization scheme needs to be addressed.     

Identification of hinging hyperplane autoregressive exogenous model using efficient mixed-integer programming

A computationally efficient algorithm for hinging hyperplane autoregressive exogenous (HHARX) model identification via mixed-integer programming technique is proposed in this paper. The HHARX model is attractive since it accurately approximates a general nonlinear process as a sum of hinge functions and preserves the continuity even in a piecewise affine form. Traditional mixed-integer programming-based method for HHARX model identification can only be applied on small-scale input/output datasets due to its significant computational demands. The contribution of this paper is to develop a sequential optimization approach to build accurate HHARX model more efficiently on a relatively large number of experimental data. Moreover, the proposed framework can handle more difficult and practical cases in piecewise model identification, such as: limited submodel switching, missing output data and specified steady state. Finally, the efficiency and accuracy of the proposed computational scheme are demonstrated through modeling of two simulated examples and a pilot-scale heat exchanger.     

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for an...     

粒子群优化的神经网络在故障诊断中的应用

为提高齿轮箱故障诊断性能，建立了以齿轮箱振动信号的时频域特征为输入，以齿轮箱的主要故障形式为输出的神经网络。采用粒子群优化算法代替反向传播算法来训练神经网络的权重和阈值，利用训练后的神经网络对齿轮箱进行了故障诊断，并比较了基于粒子群优化算法与BP算法的诊断结果。结论是基于粒子群优化算法神经网络具有较好训练性能，收敛速度快，迭代步数少，诊断精度高，具有良好的故障识别率。     

Tuning the Model Predictive Control of a Crude Distillation Unit

Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures.     

基于建模误差PDF形状的间歇过程数据驱动模型

间歇过程的优化控制依赖于过程精确的数学模型,数据驱动的建模方法是目前间歇过程模型研究中的热点问题.突破传统数据驱动建模方法中采用均方差(mean squared error,MSE)作为准则函数的思想,提出一种新颖的间歇过程数据驱动建模方法,引入了概率密度函数(probability density function,PDF)控制的概念,构造间歇过程模型误差控制系统,将模型的可调参数作为控制系统的输入,模型误差PDF的形状作为控制系统的输出,从而把开环模型参数辨识问题转化为模型误差PDF形状的闭环控制问题.通过可调参数控制模型误差PDF的空间分布状态,不仅能够保障模型精度,还可控制模型误差的空间分布状态,从而消除模型中的有色噪声.仿真实验表明,基于模型误差PDF形状的间歇过程数据驱动模型具有较好的建模精度、鲁棒性和泛化能力,为间歇过程的数据驱动建模提供了一条新途径.     

Comparison of traditional and neural network approaches to stochastic nonlinear system identification

A comparison between neural network and traditional approaches to nonlinear system identification is investigated with respect to aspects of model performance. Two neural network models, the state space and input-output model structures, are considered. A global recurrent RMLP and a teacher forcing RMLP are categorized as the state space models, and a global feedback FMLP and a teacher forcing FMLP are considered as the input-output models. In the traditional methods an AutoRegressive eXogeneous (ARX) Input model and a Nonlinear AutoRegressive eXogeneous (NARX) Input model are considered. Basic algorithms of models are described, and simulation results are also presented through the system output response. Performance of models is compared based on the Mean-Square-Errors (MSE). Noise-added sinusoidal, pulse and step signals are chosen as the test inputs for the validation of the obtained models. Two different noise levels are augmented to the chosen input signals.     

基于压缩频响函数的结构损伤识别

结构的损伤识别可作为一个优化问题来处理。本文直接应用频响函数(FRF)进行结构的损伤识别。通过对FRF的主成分分析(PCA)实现数据压缩和特征提取,建立基于压缩FRF的优化目标函数。为了提高算法的收敛速度,以结合局部搜索算法(LS)的遗传算法(GA)为优化工具,并进一步结合子结构识别法来求解。基于桁架的计算结果表明,这种方法具有很好的鲁棒性和识别效果。     

MMSE design of nonlinear Volterra equalizers using artificial bee colony algorithm

In this paper a novel approach for channel equalization is presented, where a framework for Volterra system is used to model both the channel and the equalizer. We propose development of first-order and second-order Volterra equalizers using minimum mean square error (MMSE) approach and design these equalizers using swarm intelligence based stochastic optimization algorithm which is applied to adapt the equalizer coefficients to the time varying channel. This work proposes to use the artificial bee colony (ABC) algorithm, recently introduced for global optimization, simulating the intelligent foraging behavior of honey bee swarm in a simple, robust, and flexible manner. For comparative analysis, adaptive equalizers like least mean squares (LMSs) equalizer, recursive least squares (RLSs) equalizer and least mean p-Norm (LMP) equalizer and population based optimum equalizers employing PSO are also applied for identical problems and the superiority of the newly proposed algorithm is aptly demonstrated.     

Optimization of multi-pass turning operations using hybrid teaching learning-based approach

This paper presents a novel hybrid optimization approach based on teaching–learning based optimization (TLBO) algorithm and Taguchi’s method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke–Jeeves patter search.     

Parameters identification and trajectory control for a hydraulic system

In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional–integral–derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler–Nichols (Z–N)PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller.     

Identification procedure for epistemic uncertainties using inverse fuzzy arithmetic

For the mathematical representation of systems with epistemic uncertainties, arising, for example, from simplifications in the modeling procedure, models with fuzzy-valued parameters prove to be a suitable and promising approach. In practice, however, the determination of these parameters turns out to be a non-trivial problem. The identification procedure to appropriately update these parameters on the basis of a reference output (measurement or output of an advanced model) requires the solution of an inverse problem. Against this background, an inverse method for the computation of the fuzzy-valued parameters of a model with epistemic uncertainties is presented. This method stands out due to the fact that it only uses feedforward simulations of the model, based on the transformation method of fuzzy arithmetic, along with the reference output. An inversion of the system equations is not necessary. The advancement of the method presented in this paper consists of the identification of multiple input parameters based on a single reference output or measurement. An optimization is used to solve the resulting underdetermined problems by minimizing the uncertainty of the identified parameters. Regions where the identification procedure is reliable are determined by the computation of a feasibility criterion which is also based on the output data of the transformation method only. For a frequency response function of a mechanical system, this criterion allows a restriction of the identification process to some special range of frequency where its solution can be guaranteed. Finally, the practicability of the method is demonstrated by covering the measured output of a fluid-filled piping system by the corresponding uncertain FE model in a conservative way.     

多因素耦合态下ESPCP系统输出转速的优化方法

潜油螺杆泵采油(ESPCP)系统的最优输出转速是考虑多种因素交互耦合作用的结果,是典型的非线性优化问题.应用人工神经网络技术,以MATLAB软件为平台,建立了原油粘度、泵端压差以及定转子间过盈量3个因素与输出转速之间的遗传神经网络模型.该模型运用遗传算法优化神经网络的权值和阈值,有效提高了网络的收敛性和预测的准确率.通过数据样本学习与部分现场监测数据相结合进行模拟,研究表明预测数据与实测结果基本吻合,取得了较好的效果.为考虑更多因素时优化ESPCP系统的输出转速提供了新的思路和方法.     

Study of key algorithms in topology optimization

The theory of topology optimization based on the solid isotropic material with penalization model (SIMP) method is thoroughly analyzed in this paper. In order to solve complicated topology optimization problems, a hybrid solution algorithm based on the method of moving asymptotes (MMA) approach and the globally convergent version of the method of moving asymptotes (GCMMA) approach is proposed. The numerical instability, which always leads to a non-manufacturing result in topology optimization, is analyzed, along with current methods to control it. To eliminate the numerical instability of topology results, a convolution integral factor method is introduced. Meanwhile, an iteration procedure based on the hybrid solution algorithm and a method to eliminate numerical instability are developed. The proposed algorithms are verified with illustrative examples. The effect and function of the hybrid solution algorithm and the convolution radius in optimization are also discussed.     

噪声激振下的油井动液面测量方法研究

油井动液面深度是原油开采中的关键工作参数,也是油田合理安排开采计划的重要依据。针对目前声共振法存在测量范围不足和测量精度较低的问题,论文提出了噪声激振下的油井动液面测量优化方法。本文首先探究了激励频带变化对测量结果的影响,并建立了强噪声激励下系统输出响应信号的数学模型。然后,根据建立的数学模型提出了共振信号的提取算法,通过功率谱估计和自适应同态滤波有效抑制了强噪声对共振信号的干扰。最后,研究了基于双线性插值的离散频谱校正算法,实现了共振特征参数的精确估计。实验结果表明,该方法能在强噪声干扰下提取出共振信号,实现了超过1 700 m的动液面稳定测量,且波动小于2 m。     

基于模糊神经网络的半导体生产线重调度策略优化

针对缺乏半导体生产线重调度策略优化方法的研究现状,提出了基于模糊神经网络的半导体生产线重调度策略优化技术。将重调度策略划分为半导体生产线、设备组和设备重调度层次,利用仿真评价确定优化的重调度策略,并获得样本数据。通过对模糊神经网络训练,建立干扰和半导体生产线状态等输入参数与优化的重调度策略输出之间的映射关系。以上海某125mm晶圆生产线为例,结果表明了该重调度策略优化方法的有效性。