求组合电路无冗余覆盖的立方有序扩展法

本文提出一种直接求组合电路无冗余覆盖的立方有序扩展算法。算法根据预定要求达到的目标,制订了相应的处理策略,对立方的可扩展组元(变量)排出次序,并按次序逐个对立方的可扩展组元(变量)进行扩展。本算法比以往的对扩展变量任意排序的简单立方扩展法有更优化的运算结果。     

LDPC码节点剩余度置信传播译码改进

低密度奇偶校验(LDPC)码的剩余度置信传播(RBP)和基于校验节点的剩余度置信传播(NWRBP)译码算法是根据剩余度值的有序度量,动态选择最大剩余度值所在的边或校验节点,对其依次进行更新。对比依次同步更新所有校验节点和变量节点的flooding算法,NWRBP算法的收敛速度和译码性能有了很大的提高。基于NWRBP算法,提出一种改进型NWRBP(ENWRBP)算法,即统计NWRBP译码过程中各变量节点的更新次数。如果NWRBP迭代译码失败,则将更新次数最少的变量节点的初始化值设置为0,重新译码。仿真结果表明,与NWRBP相比,ENWRBP译码算法降低了误码率和误帧率。     

对角优势的可实现性

本文根据多变量控制系统设计的逆乃氏阵列(INA)法,讨论了用常数阵对多变量控制系统传递函数阵实现对角优势的问题,并给出了相应的判据,最后还给出了一种用一阶动态阵实现对角优势的算法。     

求解作业排序问题的一种改进修复约束满足算法

修复约束满足算法(修复法)是在完整初始解的基础上不断对变量进行修复,最终得到可行解.对此,提出一种求解flow shop排序问题的改进修复法(IRCS_WT),通过采用新的变量表达方式,设计了一种以启发式优化规则为指导的变量选择算法(LWT),并采用一种变量互换算法(LTEE)保证算法的全局搜索性能.将新算法应用于31个标准算例,与传统算法及遗传算法的优化结果进行比较,结果表明在相同运算时问下改进算法具有明显的优越性.     

结合启发式算子的单变量边缘分布算法求解SAT问题

单变量边缘分布算法(UMDA)是一种新的进化算法,是求解复杂问题的一种有效算法.根据SAT问题的特点,本文提出了一种求解SAT问题的改进单变量边缘分布算法(HeUMDASAT),该算法结合SAT问题本身固有的结构信息与当前群体的优秀解所提供的全局信息,构造了一个新的启发算子,并将此算子结合到单变量边缘分布算法中.此算子不同于随机搜索算子,由其产生的个体可以使得算法跳出局部最优并探索新的潜在区域,并且加快算法的收敛速度.用SATLIB库中的标准SAT问题对HeUMDASAT算法进行测试,实验结果表明该算法在求解速度和成功率方面都有明显的改善.     

一种基于独立元贡献度的子空间故障检测方法

针对工业过程故障检测问题,本文定义了独立元贡献度和贡献度矩阵,提出一种改进的子空间检测算法.首先,利用独立元分析(independent component analysis,ICA)算法提取过程变量的独立元信息,通过计算各个独立元在过程变量上的贡献度,构造贡献度矩阵;然后根据贡献度的大小,挑选出对应的变量组成反映不同"源"信息的子空间,并在这些子空间上建立故障检测模型;最后综合以上的多个检测模型,根据实际的需求或者故障的传播特征,确定集成策略,对工业过程进行故障检测.通过在TE(Tennessee Eastman)过程上对21种故障工况和1种正常工况的仿真研究,说明提出的改进算法是有效的.     

无参分组大规模变量的多目标算法研究

目前大多数多目标优化算法没有考虑到决策变量之间的交互性,只是将所有变量当作一个整体进行优化。随着决策变量的增加,多目标优化算法的性能会急剧下降。针对上述问题,提出一种无参变量分组的大规模变量的多目标优化算法(MOEA/DWPG)。该算法将协同优化与基于分解的多目标优化算法(MOEA/D)相结合,设计了一种不含参数的分组方式来提高交互变量分组的精确性,提高了算法处理含有大规模变量的多目标优化算法的性能。实验结果表明,该算法在大规模变量多目标问题上明显优于MOEA/D及其它先进算法。     

基于局部相关性的L2Boosting算法

利用充分降维的思想对L2Boosting算法进行改进,提出基于局部相关性的L2Boosting(LCBoosting)算法。在每次迭代中,该算法根据响应变量与协变量的局部相关性充分提取信息,得到响应变量的线性组合来参与Boosting迭代,无须逐个分析所有变量。模拟结果表明,与L2Boosting算法相比,LCBoosting算法收敛速度快、预测精度高。     

NSST各向异性双变量收缩的图像去噪算法

提出一种基于非下采样Shearlet变换(NSST)的各向异性双变量收缩函数的图像去噪算法。根据NSST不同尺度间系数的方差各向异性特性,在双变量收缩函数的基础上引入各向异性拉普拉斯概率分布,利用牛顿迭代算法得到各向异性的双变量收缩函数,对NSST系数进行处理,充分利用NSST能捕捉更多纹理及结构等细节信息的优点。实验结果表明,该算法在峰值信噪比、结构相似性以及主观视觉效果上均得到较大提高。     

一种简单的旋转不变性特征量提取算法

本文以文献[1]定义的特征不变量为基础,对原文的算法进行了一定的改进.通过对其提取算法的分析,指出其特征不变量存在的缺陷,定义了一种新的特征量,消除了特征不变量的不唯一问题.实验结果和分析表明,本文算法能够在O(n)时间内提取一种特征不变量,并且所提特征不变量具有平移、旋转及比例不变特性,可以很好地用于图像目标识别.     

NOE TS fuzzy modelling of nonlinear dynamic systems with uncertainties using symbolic interval-valued data

An approach to Nonlinear Output Error (NOE) modelling using Takagi–Sugeno (TS) fuzzy model for a class of nonlinear dynamic systems having variability in their outputs is presented. Furthermore, the approach is compared and graphically illustrated with other alternate approaches on the basis of interval data and interval membership functions. Assuming the identification method can be repeated offline a number of times under similar conditions, multiple input–output time series can be obtained from the underlying system. These time series are pre-processed using the techniques of statistics and probability theory to generate the envelopes of response (curves outlining the upper and lower extremes of response) at each time instant. Two types of envelopes are described in this research: the max–min envelopes and the envelopes based on the confidence intervals provided by extended Chebyshev's inequality. By incorporating interval data in fuzzy modelling and using the theory of symbolic interval-valued data, a TS fuzzy model with interval antecedent and consequent parameters is obtained. This algorithm provides a model for predicting the expected response as well as envelopes. In order to validate the presented model, a simulation case study is devised in this paper. Moreover, it is demonstrated on the real data obtained from an electro-mechanical throttle valve.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

一种制动机气缸压力控制方法

为实现对具有非线性、时变和滞后等特性的机车制动系统的制动气缸的精确控制,提出一种气缸压力控制方法;该方法利用模糊控制领域的T-S模糊建模方法对容积室压力控制进行精确建模,通过BP算法学习得到系统的参数,利用模糊C平均聚类方法初始化模型的前件参数,采用带遗忘因子的递推最小二乘法在线修正模型的后件参数;得到系统精确的模型后再运用预测控制领域中基于模型的广义预测控制算法,实现对制动机气缸压力的精确控制;实际应用结果表明,该方法具有控制响应速度快、超调量小、自适应能力强、控制稳定等优点。     

INS algorithm using quaternion model for low cost IMU

This paper presents a generic inertial navigation system (INS) error propagation model that does not rely on small misalignment angles assumption. The modelling uses quaternions in the computer frame approach. Based on this model, an INS algorithm is developed for low cost inertial measurement unit (IMU) to solve the initial attitudes uncertainty using in-motion alignment. The distribution approximation filter (DAF) is used to implement the non-linear data fusion algorithm.     

Harmony search algorithm for minimum cost design of steel frames with semi-rigid connections and column bases

Harmony search-based algorithm is developed to determine the minimum cost design of steel frames with semi-rigid connections and column bases under displacement, strength and size constraints. Harmony search (HS) is recently developed metaheuristic search algorithm which is based on the analogy between the performance process of natural music and searching for solutions of optimum design problems. The geometric non-linearity of the frame members, the semi-rigid behaviour of the beam-to-column connections and column bases are taken into account in the design algorithm. The results obtained by semi-rigid connection and column base modelling are also compared to one developed by rigid connection modelling. The efficiency of HS algorithm, in comparison with genetic algorithms (GAs), is verified with three benchmark examples. The results indicate that HS could obtain lighter frames and less cost values than those developed using GAs.     

用神经网络建立非线性系统模型研究

本文针对多层网络结构，运用递推预报误差（ＲＰＥ）算法对离散非线性系统进行辨识研究，作为应用实例，本文对一个工业实际进行了神经网络动态建模，研究结果表明，神经网络方法是用于带有非线性特性工业过程建模的有效方法。     

Neuro-evolutionary modelling of the electrodeposition stage of a polymer-supported ultrafiltration–electrodeposition process for the recovery of heavy metals

This paper presents a neuro-evolutionary modelling methodology applied to an electrodeposition process for the recovery of copper and zinc. This technique consists in designing the optimal neural network model using an algorithm obtained through the combination of a multi-objective evolutionary algorithm (NSGA-II) and a local search algorithm (Quasi-Newton). Parametric and structural optimization for feed-forward neural networks are performed determining the optimum number of hidden layers and hidden neurons, the optimum weights and the most appropriate activation functions for the hidden and output layers. Accurate results are obtained in the modelling procedure, with the possibility to choose the adequate model, representing a compromise between performance and complexity. Significant information is obtained by simulation, related to the rate and quality of the electrodeposition process depending of the working conditions. The highest accuracy of the model is obtained for the prediction of copper and zinc concentrations (the most important output variables), a promising result to use the proposed model for the future optimization of the process. Moreover, due to the very different behaviour of copper and zinc in the electrodeposition process, the proposed model could be also successfully used for a wide variety of heavy metal ions.     

Power law-based local search in spider monkey optimisation for lower order system modelling

The nature-inspired algorithms (NIAs) have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation (SMO) algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO (PLSMO). The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.     

Application of random sets to model uncertainty of road polygons extracted from airborne laser points

High point densities obtained by today’s laser scanning systems enable the extraction of features that are traditionally mapped by photogrammetry or land surveying. While significant progress has been made in the extraction of roads from dense point clouds, little research has been performed on modelling uncertainty in extracted road polygons. In this paper random sets are used to model this uncertainty. Based on the accuracy reported by the data provider, positional errors in laser points are simulated first by a Markov Chain Monte Carlo method. An algorithm is developed next to detect the positions of road polygons in the simulated data and integrating the random sets for the uncertainty modelling. This algorithm is adapted to point data with different densities and variable distributions. Uncertainty modelling includes modelling of the dependence between the vertices of a road polygon. Road polygons constructed from vertices with different truncated normal distributions along with their uncertain line segments are represented by random sets, and their parameters are estimated. The effect of distributions on the area of the mean set is analysed and validated by a set of reference data collected from GPS measurements and image digitising. Results show that random sets provide useful spatial information on uncertainties using their basic parameters like the core, mean and support set. The study shows that random sets are well-suited to model the uncertainty of road polygons extracted from point data.