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Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System 自动巡航控制系统模糊逼近PI控制器的设计与实现
Pub Date : 2015-01-01 DOI: 10.1155/2015/624638
P. Maji, S. K. Patra, K. Mahapatra
Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.
模糊逻辑系统已被广泛应用于利用启发式知识来控制非线性和复杂的动态系统。但这些系统计算复杂,资源密集。提出了一种用于自动巡航控制系统的模糊逻辑逼近PID控制器(FLAC)的开发与移植技术。ACC是一个高度非线性过程,由于参数变化大,控制难度大。因此,基于启发式知识的合适的控制器将易于开发并提供有效的解决方案。但采用模糊控制器(FLC)的主要问题是其复杂性。此外,规则库的设计需要关于系统的有效启发式知识,而这种知识很少能找到。因此,本文采用了一种新的规则提取方法来推导FLAC。然后将该控制器移植到具有时序和内存优化的C6748 DSP硬件上。之后,它被无缝连接到网络,以支持远程可重构性。利用Simulink模型对某型汽车巡航控制系统进行了处理器在环测试,并进行了性能分析。
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引用次数: 1
Pop-Out: A New Cognitive Model of Visual Attention That Uses Light Level Analysis to Better Mimic the Free-Viewing Task of Static Images 弹出:一种新的视觉注意认知模型,使用光照水平分析来更好地模仿静态图像的自由观看任务
Pub Date : 2015-01-01 DOI: 10.1155/2015/471483
Makiese Mibulumukini
Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider light level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics physiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model analyzes lighting conditions with the aim of giving different weights to color wavelengths. The resulting saliency measure performs better than a lot of popular cognitive approaches.
当光照条件改变时,人的视线不会指向图像的同一部分。目前的显著性模型在自下而上的过程中不考虑光照水平分析。本文介绍了一种新的显著性模型,该模型更好地模拟了自由观看任务(自下而上过程)下视觉注意的生理和心理过程。该模型分析光照条件,目的是为颜色波长赋予不同的权重。由此产生的显著性测量比许多流行的认知方法表现得更好。
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引用次数: 2
A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration 基于Dirichlet过程混合的人名起源聚类与音译对齐模型
Pub Date : 2015-01-01 DOI: 10.1155/2015/927063
Chunyue Zhang, T. Zhao, Tingting Li
In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM.
在机器音译中,目标语言中的音译名称通常来自多个语言来源。传统的基于最大似然的单一模型不能很好地处理这一问题,并且经常出现过拟合的问题。本文利用一个耦合的Dirichlet过程混合模型(cDPMM)来同时解决名称对的音译序列比对步骤中的过拟合和名称多源聚类问题。在对齐步骤之后,cDPMM集群自动根据它们的原始信息将它们分成许多组。在解码步骤中,为了充分利用学习到的源信息,我们基于名称语言和音译模型的困惑度,采用聚类组合方法(CCM)将小聚类组合成大聚类,构建了特定于聚类的音译模型,确保每个源聚类都有足够的数据用于训练音译模型。在三种不同的中西方多源名称语料库上,cDPMM在top-1准确率和平均F-score方面都优于两种最先进的基线模型,CCM显著提高了cDPMM。
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引用次数: 1
Study on Similarity among Indian Languages Using Language Verification Framework 基于语言验证框架的印度语言相似性研究
Pub Date : 2015-01-01 DOI: 10.1155/2015/325703
D. Sengupta, G. Saha
Majority of Indian languages have originated from two language families, namely, Indo-European and Dravidian. Therefore, certain kind of similarity among languages of a particular family can be expected to exist. Also, languages spoken in neighboring regions show certain similarity since there happens to be a lot of intermingling between population of neighboring regions. This paper develops a technique to measure similarity among Indian languages in a novel way, using language verification framework. Four verification systems are designed for each language. Acceptance of one language as another, which relates to false acceptance in language verification framework, is used as a measure of similarity. If language A shows false acceptance more than a predefined threshold with language B, in at least three out of the four systems, then languages A and B are considered to be similar in this work. It is expected that the languages belonging to the same family should manifest their similarity in experimental results. Also, similarity between neighboring languages should be detected through experiments. Any deviation from such fact should be due to specific linguistic or historical reasons. This work analyzes any such scenario.
大多数印度语言起源于两个语系,即印欧语系和德拉威语系。因此,一个特定语系的语言之间可以预期存在某种相似性。此外,邻近地区的语言表现出一定的相似性,因为邻近地区的人口之间碰巧有很多混杂。本文提出了一种基于语言验证框架的印度语言相似性度量方法。每种语言设计了四种核查系统。将一种语言接受为另一种语言,这与语言验证框架中的错误接受有关,它被用作相似性的衡量标准。如果在四个系统中的至少三个系统中,语言A对语言B的错误接受程度超过了预定义的阈值,则认为语言A和语言B在本工作中是相似的。期望同属一科的语言在实验结果中表现出相似性。另外,要通过实验来检测邻近语言之间的相似性。任何与这一事实的偏差都应归因于特定的语言或历史原因。这项工作分析了任何这样的情况。
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引用次数: 22
Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification 小波网络:非线性动态系统辨识的在线顺序极限学习机
Pub Date : 2015-01-01 DOI: 10.1155/2015/184318
D. Salih, S. Noor, M. Marhaban, R. Kamil
A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.
采用在线顺序极值学习机(OSELM)算法的单隐层前馈神经网络(SLFN)已被引入并成功地应用于许多回归问题。然而,使用带有OSELM的SLFN作为非线性系统辨识的黑盒,可能会导致从控制角度对响应不一致的被辨识对象建立模型。原因可以参考使用OSELM算法随机初始化SLFN隐藏节点参数的过程。本文将单隐层前馈小波网络(WN)与OSELM相结合,用于非线性系统辨识,旨在通过减少随机初始化过程的影响,获得更好的泛化性能。
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引用次数: 6
Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data 两种基于人工神经网络的截尾数据离散生存时间建模
Pub Date : 2015-01-01 DOI: 10.1155/2015/270165
Taysseer Sharaf, C. Tsokos
Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.
人工神经网络(ANN)理论作为传统统计方法在非线性函数建模方面的替代方法正在兴起。流行的Cox比例风险模型在模拟具有非线性行为的生存数据方面存在不足。由于不需要风险假设和模型松弛的比例性,人工神经网络是Cox PH的一个很好的替代方法。此外,人工神经网络具有处理与生存时间相关的危险因素之间复杂非线性关系的强大能力。在本研究中,我们全面比较了两种利用人工神经网络建模光滑条件风险概率函数的不同方法。我们使用真实的黑色素瘤癌症数据来说明所提出的人工神经网络方法的有效性。我们在比较男性和女性黑色素瘤患者的生存时间方面报告了一些显著的结果。
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引用次数: 10
Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones 数字信息素综合增强环境中的强化学习
Pub Date : 2014-03-13 DOI: 10.1155/2014/932485
Salvador E. Barbosa, Mikel D. Petty
Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes.
强化学习需要关于状态、行为和结果的信息作为学习的基础。对于许多应用程序,构建环境的代表性模型可能很困难,原因可能是缺乏所需的信息,或者是因为模型的状态空间可能变得太大,无法使用先前操作的经验在合理的时间内找到解决方案。仅由可归因于人类行为者的特定事件的发生或不发生组成的环境可能缺乏使用强化学习在时间和空间上定位响应代理的必要结构。数字信息素可以用事件序列信息来综合增强这样的环境,从而在支持强化学习的环境上创建更持久和可测量的印记。我们实现了这种方法,并将其与智能体从未采取的行动中学习的能力相结合,这是一个被称为有效学习的概念。该方法在2005年至2012年中期的索马里海盗袭击历史序列中进行了测试,使一组代表海军舰艇的自主代理成功应对了899次海盗袭击中的333次,超过了139次成功的历史记录。
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引用次数: 8
Design of a T Factor Based RBFNC for a Flight Control System 基于T因子的RBFNC飞控系统设计
Pub Date : 2014-01-01 DOI: 10.1155/2014/897691
C. Mohanty, P. S. Khuntia, D. Mitra
This paper presents the design of modified radial basic function neural controller (MRBFNC) for the pitch control of an aircraft to obtain the desired pitch angel as required by the pilot while maneuvering an aircraft. In this design, the parameters of radial basis function neural controller (RBFNC) are optimized by implementing a feedback mechanism which is controlled by a tuning factor "α" (T factor). For a given input, the response of the RBFN controller is tuned by using T factor for better performance of the aircraft pitch control system. The proposed system is demonstrated under different condition (absence and presence of sensor noise). The simulation results show that MRBFNC performs better, in terms of settling time and rise time for both conditions, than the conventional RBFNC. It is also seen that, as the value of the T factor increases, the aircraft pitch control system performs better and settles quickly to its reference trajectory. A comparison between MRBFNC and conventional RBFNC is also established to discuss the superiority of the former techniques.
提出了一种改进型径向基函数神经控制器(MRBFNC),用于飞机俯仰控制,以获得飞行员在操纵飞机时所需的俯仰角。在本设计中,径向基函数神经控制器(RBFNC)的参数通过一个由调谐因子“α”(T因子)控制的反馈机制进行优化。对于给定的输入,利用T因子对RBFN控制器的响应进行调谐,以提高飞机俯仰控制系统的性能。在不同的条件下(无噪声和存在噪声)对该系统进行了验证。仿真结果表明,在两种情况下,MRBFNC的沉降时间和上升时间都优于传统的RBFNC。还可以看出,随着T因子的增大,飞机俯仰控制系统的性能越好,越快地稳定在参考轨迹上。并将MRBFNC与常规RBFNC进行了比较,讨论了前者技术的优越性。
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引用次数: 5
Estimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah 利用GEP估算缺失降雨数据:以吉打州Alor Setar Raja河为例
Pub Date : 2014-01-01 DOI: 10.1155/2014/716398
N. Ghani, Z. A. Hasan, T. L. Lau
Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R2 is 0.886.
水资源和城市洪水管理需要水文和水力建模。然而,在水文模拟过程中,降水数据不完整往往是一个问题。在这项研究中,利用基因表达编程(GEP)将位于马来西亚吉打州Alor Setar的主要气象站的月降水数据与邻近气象站的月降水数据相关联。遗传规划是遗传规划的一种扩展,可以提供简单有效的求解方法。研究说明了GEP在确定代替主雨量站(6103047)的最适宜雨量站中的应用。这是为了确保在主站出现故障时,可以建立一个可靠的雨量站。这些是通过比较主站数据与每个邻近站点的数据来完成的。分析结果表明,38号台站与主台站最相容,R2为0.886。
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引用次数: 5
A New Evolutionary-Incremental Framework for Feature Selection 一种新的进化-增量特征选择框架
Pub Date : 2014-01-01 DOI: 10.1155/2014/679847
M. Sigari, Muhammad Reza Pourshahabi, H. Pourreza
Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection.The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length.Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.
从算法设计的角度来看,特征选择是一个np困难问题,是模式识别中的主要开放性问题之一。在本文中,我们提出了一种新的进化-增量特征选择框架。该框架可以应用于遗传算法(GA)或入侵杂草优化(IWO)等普通进化算法(EA)。该框架提出了对普通ea的一些通用修改,以兼容解的可变长度。在这个框架中,与初级代相关的解具有较短的长度。然后,解的长度可以通过代逐渐增加。此外,我们的进化增量框架部署了两个新的操作符,称为添加和删除操作符,它们随机改变解的长度。为了评估所提出的框架,我们将其用于人脸识别应用中的特征选择。在这方面,我们将我们的特征选择方法应用于基于Gabor系数提取的鲁棒人脸识别算法。实验结果表明,我们提出的进化增量框架可以有效地从现有的数千个特征中选择少量的特征。与已有方法的比较结果表明,该框架具有较强的鲁棒性和良好的定义,可以应用于多种ea的特征选择。
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引用次数: 3
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