Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Acid-pickling plates and strips speed control system by microwave heating based on self-adaptive fuzzy PID algorithm

Double self-adaptive fuzzy PID algorithm-based control strategy was proposed to construct quasi-cascade control system to control the speed of the acid-pickling process of titanium plates and strips. It is very useful in overcoming non-linear dynamic behavior, uncertain and time-varying parameters, un-modeled dynamics, and couples between the automatic turbulence control (ATC) and the automatic acid temperature control (AATC) with varying parameters during the operation process. The quasi-cascade control system of inner and outer loop self-adaptive fuzzy PID controller was built, which could effectively control the pickling speed of plates and strips. The simulated results and real application indicate that the plates and strips acid pickling speed control system has good performances of adaptively tracking the parameter variations and anti-disturbances, which ensures the match of acid pickling temperature and turbulence of flowing with acid pickling speed, improving the surface quality of plates and strips acid pickling, and energy efficiency.

Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm

A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary calculations.Furthermore,the crossover probability and scaling factor are self-adaptively controlled.FLUKA Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor spectrum.Subsequently,the measured neutron counts are unfolded using the SDENUA.The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default spectrum.The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.

An improved self-adaptive membrane computing optimization algorithm and its applications in residue hydrogenating model parameter estimation

In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.

Enhanced self-adaptive evolutionary algorithm for numerical optimization

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm （ESEA） to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

基于VDFE-RLS的单波长200 Gbit/s光接入系统(特邀)

【目的】高速无源光接入网络中存在光纤色散、非线性损伤和带宽限制等问题,导致传统强度调制和直接检测技术的功率预算损失较高,难以满足高速无源光接入网络的要求。【方法】为了更好地提升强度调制和直接检测光接入系统的速率和性能,文章在Volterra判决反馈均衡器(VDFE)的基础上,研究了基于递推最小二乘估计(RLS)算法的VDFE-RLS信道均衡方法。该均衡器采用RLS算法对其中的抽头系数进行更新。该均衡器包含了一、二、三阶Volterra级数,其中一阶Volterra级数对线性损伤进行补偿,二阶和三阶Volterra级数能够对非线性损伤进行补偿。文章将该均衡器应用于经过20 km传输后的单波长为200 Gbit/s的O波段强度调制和直接检测技术的下行光接入系统中。【结果】实验结果表明,RLS算法相比传统的最小均方(LMS)算法在均衡器中表现出来的性能更好。此外,VDFE-RLS可以实现>29 dB的功率预算。VDFE-RLS相比于传统的基于Volterra的前馈均衡器(VFFE),当VDFE-RLS和VFFE-RLS均衡器长度相同时,可以实现2.2 dB功率预算的提升。当VDFE-RLS的均衡器长度为VFFE-RLS的一半时,前者相比后者仍可以提升0.5 dB的功率预算。【结论】文章所述系统相比其他传统系统在能够缩短均衡器长度的同时,能提高系统的功率预算,还能最终恢复出准确度较高的信号。

Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.

基于复数域前向放松QRD-RLS算法的脉动阵结构实现

现代军用平台有限空间内集成了大量射频收发天线系统,导致共址电磁自兼容问题严重,干扰对消技术是解决共址同频干扰问题的一种重要手段。高跳速、环境剧烈变化场合对对消系统的处理速度提高了更高要求。论文提出了一种基于复数域QRD-RLS算法的数字域快速对消算法,推导出了其高速脉动阵实现结构,并在Xilinx FPGA上研究了实现方法。仿真和实测结果表明,高速脉动实现结构实时处理时钟频率可达60 MHz,可有效实现干扰对消功能,具有良好的工程实现度。

基于神经网络与RLS算法的短波信道均衡研究

针对短波信道中信号传输产生的码间串扰问题,文中结合人工神经网络(Artificial Neural Network,ANN)与递归最小二乘法(Recursive Least Square,RLS)算法,实现改进的RLS-ANN短波信道均衡算法。通过有监督与无监督分段均衡处理,实时校准经由短波信道传播之后产生的信号幅度和相位失真。对实测的高频数据链(High Frequency Data Link,HFDL)短波信号进行均衡处理,结果表明RLS-ANN算法相比传统的最小均方算法(Least Mean Square,LMS)和RLS算法在星座图收敛速度、平均误差及误码率等方面效果更优,该算法通过降低误码率,可有效改善信号经由短波信道传输的通信质量。

基于RLS和EKF算法的锂离子动力电池荷电状态估计

电池荷电状态(SOC)是电动汽车电池管理系统的关键参数之一,影响着整车性能与安全。文章以一阶Thevenin等效电路作为电池模型,采用递推最小二乘法(RLS)对电池进行参数辨识,再运用扩展卡尔曼滤波(EKF)算法估算电池的SOC。将估算结果与试验测量结果进行对比,结果显示,RLS-EKF的联合算法可有效估计电池的SOC值,估算误差值基本保持在2%以内。

Generalized Self-Adaptive Genetic Algorithms

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.

Method for improving RLS algorithms

The recursive least-square (RLS) algorithm has been extensively used in adaptive identification, prediction, filtering, and many other fields. This paper proposes adding a second-difference term to the standard recurrent formula to create a novel method for improving tracing capabilities. Test results show that this can greatly improve the convergence capability of RLS algorithms.

Differential Evolution Algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

In the fed-batch cultivation of Saccharomyces cerevisiae,excessive glucose addition leads to increased ethanol accumulation,which will reduce the efficiency of glucose utilization and inhibit product synthesis.Insufficient glucose addition limits cell growth.To properly regulate glucose feed,a different evolution algorithm based on self-adaptive control strategy was proposed,consisting of three modules(PID,system identification and parameter optimization).Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations.In the simulation,cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration,more stable ethanol concentration around the set-point(1.0 g·L^(-1)),and final biomass concentration of 34.5 g-DCW·L^(-1),29.2%higher than that with a conventional PID control strategy.In the experiment,the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations,as well as a final biomass concentration that was 37.4%higher than that using the conventional strategy.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Self-Adaptive Algorithms for the Split Common Fixed Point Problem of the Demimetric Mappings

The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper, we present new iterative algorithms for solving the split common fixed point problem of demimetric mappings in Hilbert spaces. Moreover, our algorithm does not need any prior information of the operator norm. Weak and strong convergence theorems are given under some mild assumptions. The results in this paper are the extension and improvement of the recent results in the literature.

Blind cancellation for frequency offset in OFDM system based on MCMA-RLS algorithm

Modified constant modulus and recursive least squares （MCMA-RLS） algorithm is proposed to cancel interference caused by the variable frequency offset （FO） in the orthogonal frequency division multiplexing （OFDM） system. The MCMA-RLS algorithm is composed of two stages including MCMA scheme and RLS scheme. MCMA is selected to pre-cancel the variable frequency offset firstly, and then the residual interference has been canceled by the RLS scheme. BR error rate is simulated to demonstrate that the proposed method is robust for canceling the variable frequency offset.

采用RLS算法的功率倒置阵列的性能

为解决功率倒置阵列采用LMS算法时收敛速度和稳态失调误差不能兼顾的缺点,提出了采用RLS算法实现功率倒置阵列的方法。仿真结果表明,功率倒置阵列采用RLS算法和LMS算法都能达到较好的抗干扰效果。和LMS算法相比,RLS算法同时具有收敛速度快和稳态失调误差小的优点,代价是计算量增大。