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2005 ICSC Congress on Computational Intelligence Methods and Applications最新文献

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A comparison of fuzzy, state space with direct eigenstructure assignment, and PID controller on linearized MIMO plant model 线性化MIMO对象模型的模糊、状态空间直接特征结构分配与PID控制器的比较
Pub Date : 2008-01-28 DOI: 10.1109/CIMA.2005.1662347
D. Linarić, T. Kostic, V. Koroman
Very often technical systems work very close to stationary working conditions, quasi stationary conditions. For example thermal power plant, steam turbine system mostly working under quasi stationary working conditions. Bearing in mind that fact, the idea is to design control structure optimal for that working conditions. For this purpose, state space controller with direct eigenstructure assignment is designed and compared with fuzzy and PID in Linarie, D., (2002) controller on linearized MIMO model of power plant, steam turbine
很多时候,技术系统的工作非常接近于固定的工作条件,准固定的工作条件。例如火力发电厂,汽轮机系统大多在准静止工况下工作。记住这个事实,我们的想法是设计出最适合工作条件的控制结构。为此,设计了直接特征结构赋值的状态空间控制器,并与Linarie, D.(2002)对电厂、汽轮机线性化MIMO模型的模糊控制器和PID控制器进行了比较
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引用次数: 0
On-line reactor monitoring with neural network for RSG-GAS 基于神经网络的RSG-GAS反应器在线监测
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662354
K. Nabeshima, K. Kurniant, T. Surbakti, S. Pinem, M. Subekti, Y. Minakuchi, K. Kudo
The ANNOMA (artificial neural network of monitoring aids) system is applied to the condition monitoring and signal validation of multi purpose reactor (RSG-GAS) in Indonesia. The feedforward neural network in auto-associative mode learns reactor's normal operational data, and models the reactor dynamics during the initial learning. The basic principle of the anomaly detection is to monitor the deviation between the process signals measured from the actual reactor and the corresponding values predicted by the reactor model, i.e., the neural networks. The pattern of the deviation at each signal is utilized for the identification of anomaly, e.g. sensor failure or system fault. The on-line test results showed that the neural network successfully monitored the reactor status during power increasing and steady state operation in real-time
将人工神经网络监测辅助系统(ANNOMA)应用于印尼多用途反应堆(RSG-GAS)的状态监测和信号验证。采用自关联模式的前馈神经网络学习反应堆的正常运行数据,并在初始学习过程中对反应堆的动力学进行建模。异常检测的基本原理是监测实际反应釜测量到的过程信号与反应釜模型(即神经网络)预测的相应值之间的偏差。利用每个信号的偏差模式来识别异常,例如传感器故障或系统故障。在线试验结果表明,该神经网络能够实时监测电抗器在增功率和稳态运行过程中的状态
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引用次数: 7
Matrix representation and implementation of fuzzy system 模糊系统的矩阵表示与实现
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662336
Z. Miao, Xiangyu Zhao
Fuzzy logic is wildly used in many fields in the recent years. It is also the theoretic base of fuzzy control. A novel matrix representation and implementation method is prompted in this paper. The new method employs the concepts of state space which achieved great success in the modern control theory and uses matrix to represent fuzzy models including the fuzzification, inference mechanism, rule base and defuzzification. Some new combining operators for fuzzy logic inference are also defined in this paper. To show the correctness and efficiency of the new method, a nonlinear system is discussed employing the new methods
近年来,模糊逻辑在许多领域得到了广泛的应用。这也是模糊控制的理论基础。提出了一种新的矩阵表示和实现方法。该方法采用现代控制理论中非常成功的状态空间概念,用矩阵表示模糊模型,包括模糊化、推理机制、规则库和去模糊化。本文还定义了一些新的模糊逻辑推理组合算子。为了证明新方法的正确性和有效性,用新方法对一个非线性系统进行了讨论
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引用次数: 1
Hybrid neural-fuzzy modeling for impact toughness prediction of alloy steels 基于神经-模糊混合模型的合金钢冲击韧性预测
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662335
M.-Y. Chen, D. Linkens
As one of the most important characteristics of structural steels, toughness is assessed by the Charpy V-notch impact test. The absorbed impact energy and the transition temperature defined at a given Charpy energy level are regarded as the common criteria for toughness assessment. This paper aims at establishing generic toughness prediction models which link materials compositions and processing conditions with Charpy impact properties. Hybrid knowledge-based neural-fuzzy modeling techniques which incorporate linguistic knowledge into data-driven neural-fuzzy models have been used to develop the Charpy properties prediction models for thermomechanically controlled rolled (TMCR) steels. Two basic ways of knowledge incorporation are introduced to improve the performance of the obtained fuzzy models. Simulation experiments show that both numeric data and linguistic information can be combined in a unified framework and that both Charpy impact energy and the impact transition temperature (ITT) can be predicted by the same model
作为结构钢最重要的特性之一,韧性是通过夏比v形缺口冲击试验来评估的。将吸收的冲击能和在给定夏比能级下定义的转变温度作为评定韧性的常用标准。本文旨在建立将材料成分、加工条件与夏比冲击性能联系起来的通用韧性预测模型。将语言知识与数据驱动的神经模糊模型相结合的基于知识的混合神经模糊建模技术已被用于开发热控轧钢(TMCR)的Charpy性能预测模型。介绍了两种基本的知识整合方法,以提高所得到的模糊模型的性能。仿真实验表明,该方法可以将数值数据和语言信息结合在一个统一的框架中,并且可以用同一模型预测Charpy冲击能和冲击转变温度(ITT)
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引用次数: 1
Color-coating production scheduling in the steel industry 钢铁行业彩色涂料生产调度
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662312
Xianpeng Wang, Lixin Tang
This paper proposes a new integer programming model and a tabu search heuristics for large-scale scheduling in the iron and steel industry, called color-coating production scheduling (CCPS). The results obtained from real production instances show that the model and heuristics are more effective and efficient with comparison to the manual scheduling
针对钢铁工业中大规模调度问题,提出了一种新的整数规划模型和禁忌搜索启发式算法——彩涂生产调度。实际生产实例的结果表明,与人工调度相比,该模型和启发式算法更加有效
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引用次数: 1
Learning from genome sequences utilizing computational intelligence 利用计算智能从基因组序列中学习
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662343
J.Y. Yang, M.Q. Yang, O. Ersoy
Advances in genome sequencing technology have led to an exploration in the amount of sequence data available, learning from proteins coded for by genomes is a difficult task. Bioinformatics is thus a burgeoning field that holds great promise for deepening our understanding of biochemical pathways, for understanding the genetic differences between species and how they arose, and for understanding the genetic basis of various disease processes. We developed a method for classification and knowledge discovery in membrane and intrinsic unstructured/disordered proteins (IUP). We analyzed the amino acid compositions and biophysical properties of proteins. Our joint transmembrane and IUP predictor utilized biophysical characterizations, feature generation, feature selection and computational intelligence as well as ensemble methods to improve the accuracies and performances
基因组测序技术的进步导致了可用序列数据量的探索,从基因组编码的蛋白质中学习是一项艰巨的任务。因此,生物信息学是一个新兴的领域,它对加深我们对生化途径的理解,理解物种之间的遗传差异及其产生方式,以及理解各种疾病过程的遗传基础有着巨大的希望。我们开发了一种膜和内在非结构/无序蛋白(IUP)的分类和知识发现方法。我们分析了蛋白质的氨基酸组成和生物物理性质。我们的联合跨膜和IUP预测器利用生物物理表征、特征生成、特征选择和计算智能以及集成方法来提高准确性和性能
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引用次数: 0
Index tracking using a hybrid genetic algorithm 使用混合遗传算法的索引跟踪
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662364
Roland Jeurissen, J. V. D. Berg
Assuming the market is efficient, an obvious portfolio management strategy is passive where the challenge is to track a certain benchmark like a stock index. The goal of the passive strategy is to achieve equal returns and risks. In this paper, we investigate an approach for tracking the Dutch AEX index where an optimal tracking portfolio (consisting of a weighted subset of stock funds) is determined. The optimal weights of a portfolio are found by minimizing the tracking error for a set of historical returns and covariances. The overall optimal portfolio is found using a hybrid genetic algorithm where the fitness function of each chromosome (possible subset of stocks) equals the minimal tracking error achievable. We show the experimental setup and the simulation results, including the out-of-sample performance of the optimal tracking portfolio found
假设市场是有效的,一个明显的投资组合管理策略是被动的,其中的挑战是跟踪某个基准,如股票指数。被动策略的目标是获得同等的收益和风险。在本文中,我们研究了一种跟踪荷兰AEX指数的方法,其中确定了最优跟踪投资组合(由股票基金的加权子集组成)。通过最小化一组历史收益和协方差的跟踪误差来找到投资组合的最优权重。采用混合遗传算法,使每条染色体(可能的股票子集)的适应度函数等于可实现的最小跟踪误差,从而找到总体最优投资组合。我们展示了实验设置和仿真结果,包括找到的最优跟踪组合的样本外性能
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引用次数: 19
Design of congestion controller for ATM networks via adaptive control law 基于自适应控制律的ATM网络拥塞控制器设计
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662349
A. Riazi, F. Habibipour, M. Galily
Proportional control methods of controlling congestion in high speed ATM networks fail to achieve the desired performance due to the action delays, nonlinearities, and uncertainties in control loop. In this paper an adaptive minimum variance controller is proposed to minimize the rate of stochastic inputs from uncontrollable high priority sources. This method avoids the computations needed for pole placement design of the minimum variance controller, and utilizes an online recursive least squares algorithm in direct tuning of the controller parameters. The closed loop system is adaptive and robust to the uncertain network conditions and provides minimum cell loss ratio, efficient use of network resources, and fair allocation of the available bandwidth through the controlled sources
在高速ATM网络中,控制拥塞的比例控制方法由于控制回路存在动作延迟、非线性和不确定性等问题,无法达到理想的控制效果。本文提出了一种自适应最小方差控制器,以最小化不可控高优先级源的随机输入率。该方法避免了最小方差控制器极点布置设计所需的计算,并利用在线递归最小二乘算法直接整定控制器参数。闭环系统对不确定的网络条件具有较强的适应性和鲁棒性,并能提供最小的小区损失率、有效利用网络资源和通过可控源公平分配可用带宽
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引用次数: 1
Improved tabu search algorithms for storage space allocation in integrated iron and steel plant 基于改进禁忌搜索算法的综合钢铁厂存储空间分配
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662319
Shaohua Li, Lixin Tang
The fact that diversified and enormous materials are usually stored in open yards adds extra difficulty to model and solve the storage space allocation problem in material yards of iron and steel plants. This paper presents a nonlinear mathematical model for such a problem with the objective function of minimizing transportation costs and penalty trigged by the difference between materials and develops improved tabu search algorithms to solve it. These algorithms have two diversification strategies: (1) using an iterated local search strategy based on random kick moves as a method to escape from local optima and the neighborhood of descent heuristic in the iterated local search is generated by cyclic exchange moves; (2) directly using a cyclic exchange move to guide the search to a solution outside the neighborhood of a local optimum. The test with 150 random data sets proves that the tabu search with new diversification strategies is a fast and effective near optimal algorithm to solve such a practical industry problem
由于露天堆场储存的物料种类繁多,体积庞大,这给建立和解决钢铁厂物料堆场的存储空间分配问题增加了额外的难度。本文建立了以物料差异引起的运输成本和处罚最小为目标函数的非线性数学模型,并提出了改进的禁忌搜索算法来求解该问题。这些算法有两种多样化策略:(1)采用基于随机踢步的迭代局部搜索策略作为逃避局部最优的方法,迭代局部搜索中的下降启发式邻域由循环交换步产生;(2)直接使用循环交换移动来引导搜索到局部最优邻域之外的解。150个随机数据集的测试证明了禁忌搜索新多样化策略是解决这类实际行业问题的一种快速有效的近最优算法
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引用次数: 1
Automatic honeycombing detection using texture and structure analysis 基于纹理和结构分析的自动蜂窝检测
Pub Date : 2005-12-15 DOI: 10.1109/CIMA.2005.1662333
James S. J. Wong, T. Zrimec
Honeycombing in the lung is an important diagnostic sign for diseases involving fibrosis of the lung. Furthermore, the quantification of honeycombing is needed to determine the severity of the disease. In this paper, we present a novel method of automatically detecting honeycombing regions in high resolution computed tomography images of the lung. We detect potential honeycombing cysts within the lung boundary and cluster them based on Euclidean distance. The texture attributes of the cluster region are then calculated. We also use the regional information of the cluster as honeycombing occurs predominantly in the peripheral region of the lung. This regional information has not been used in any of the literature reported and allows us to distinguish honeycomb cysts from other similar looking structures such as the bronchi. A decision tree is generated using the Weka J48 algorithm, with the training examples supplied by the radiologist. The decision tree is then used in the automatic classification of honeycombing regions. The classification performance is evaluated by comparing against the honeycombing regions provided by the radiologist
肺蜂窝状是肺纤维化疾病的重要诊断征象。此外,为了确定疾病的严重程度,需要对蜂巢进行量化。在本文中,我们提出了一种新的方法,自动检测蜂窝状区域的高分辨率计算机断层扫描图像的肺。我们检测肺边界内潜在的蜂窝状囊肿,并基于欧几里得距离对其进行聚类。然后计算聚类区域的纹理属性。我们还使用集群的区域信息,因为蜂窝主要发生在肺的周围区域。这一区域信息尚未在任何文献报道中使用,使我们能够将蜂窝囊肿与其他类似的结构(如支气管)区分开来。使用Weka J48算法生成决策树,并使用放射科医生提供的训练示例。然后将决策树用于蜂窝区域的自动分类。通过与放射科医生提供的蜂窝状区域进行比较来评估分类性能
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引用次数: 7
期刊
2005 ICSC Congress on Computational Intelligence Methods and Applications
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