检索结果-内蒙古大学图书馆

Model predictive control of an autonomous underwater vehicle with a fuzzy objective function optimized using a GA

IFAC Proceedings Volumes 2004年第10期37卷 433-438页

作者： W. Naeem R. Sutton J. Chudley Marine and Industrial Dynamic Analysis Research Group Reynolds Building School of Engineering University of Plymouth Drake Circus Plymouth PL4 8AA UK

Recently, unmanned underwater vehicles (UUVs) have emerged as a viable tool for ocean exploration and for military purposes. This is due to the inability of human divers to reach deep sea and the hostile nature of underwater environment. UUVs are of two types, namely, remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs). This paper is concerned with the control of an AUV. A model predictive controller is developed herein where the traditional cost function has been replaced by a fuzzy performance index which represent the goals and constraints of the problem. Since fuzzy logic is basically derived from knowledge of human expertise, it is therefore more intuitive than a conventional cost function. Moreover, the choice of aggregation operator can lead to significant reduction in tuning time which is essential for a quadratic objective function. A genetic algorithm (GA) is used as an optimization tool to evaluate the control inputs by minimization of the performance index represented by fuzzy membership values. The resulting controller is applied to an AUV simulation model obtained from system identification techniques on test trials data. Simulation results are presented that demonstrate the efficacy of the approach.

关键词： Model predictive control genetic algorithm fuzzy objective function optimization and underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Optimal solutions in optimization problem with objective function depending on fuzzy parameters

引用

fuzzy SETS AND SYSTEMS 2007年第17期158卷 1873-1881页

作者： Ramik, Jaroslav Silesian Univ Fac Business Adm Dept Math Methods Econ Karvina Czech Republic

In this paper we first introduce a broad class of fuzzy optimization problems with an objective function depending on fuzzy parameters (FO problems) and introduce the concept a-maximal solutions of FO problems with respect to some fuzzy relation. We derive a simple and natural sufficient condition for a feasible solution to be an a-maximal solutions of FO problem. Later on we define possibly and necessarily a-maximal solutions of FO problems and prove that any possibly or necessarily a-maximal solution of FO problem is an a-maximal solution with respect to the possibilistic fuzzy relation or necessity fuzzy relation, respectively. The introduced concepts and results are illustrated and discussed on two numerical examples. (C) 2007 Elsevier B.V. All rights reserved.

关键词： fuzzy optimization fuzzy objective function fuzzy parameters possibly and necessarily optimal solution

来源：评论

学校读者我要写书评

暂无评论

Optimizing Production Mix Involving Linear Programming with fuzzy Resources and fuzzy Constraints

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 727-733页

作者： Onasanya, B. O. Feng, Y. Wang, Z. Samakin, O., V Wu, S. Liu, X. Chongqing Three Gorges Univ Key Lab Intelligent Informat Proc & Control Wenzhou 404100 Peoples R China Chongqing Three Gorges Univ Chongqing Engn Res Ctr Internet Things & Intellig Wenzhou 404100 Peoples R China Chongqing Three Gorges Univ Sch Three Gorges Artificial Intelligence Wenzhou 404100 Peoples R China Univ Ibadan Ibadan 200284 Oyo Stale Nigeria Chongqing Three Gorges Univ Sch Three Gorges Rig Data Wenzhou 404100 Peoples R China Chongqing Three Gorges Univ Sch Comp Sci & Engn Wenzhou 404100 Peoples R China

In this paper, fuzzy Linear Programming (FLP) was used to model the production processes at a university-based bakery for optimal decisions in the daily productions of the bakery, Using the production data of five products from the bakery., a fuzzy linear programme was developed to help make decisions when fuzzy resources were involved. As usual, classical linear programme gave only one feasible solution. However, while it was established that solving the linear programme from the production mix when fuzzy constraints were introduced (Verdegay's model) gave a more robust and alternative set of decisions than the classical model, it was equally established that a better result could be obtained when all the constraints and the objective function were fuzzy (Werners' Model). (C) 2020 The Authors. Published by Atlantis SARL.

关键词： fuzzy resources fuzzy constraints fuzzy objective function fuzzy optimization

来源：评论

学校读者我要写书评

暂无评论

Epistemic uncertainty based linear programming problem and its solution

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第6期15卷 2337-2346页

作者： Behera, Diptiranjan Thomas, Romane Univ West Indies Dept Math Kingston 7 Jamaica

Epistemic uncertainty such as fuzzy based linear programming problem with equality constraints has been considered here. Fuzziness presents in system parameters are modelled using Trapezoidal fuzzy Number (TrFN). In the considered problem the coefficients are defined as crisp while the decision variables as well as the right-hand side of the constraints are taken as fuzzy. Accordingly, a new method to solve the problem based on the fuzzy centre and radius has been proposed here. First the problem is solved for fuzzy centre and next the bounds of the fuzzy variable are replaced in terms of fuzzy centre and radius. Then using the obtained fuzzy centre solution there one can have the radius of the solution. Finally using the results for centre and radius one can get the final solution. For the implementation of this proposed methodology LINGO 18.0 software has been used. Consequently, optimal fuzzy feasible solution has been obtained to get the optimal value (maximum/minimum) of the fuzzy objective function. Moreover, various numerical examples have been solved using the proposed method and the obtained solutions are compared with the solution of existing methods for validation. Moreover, it has been observed that present methods overcome the limitations of Maleki (Maleki in Far East J Math Sci 4:283-301, 2002), Nasseri (Nasseri in Appl Math Sci 2:2473-2480, 2008), Mahdavi-Amiri and Nasseri (Mahdavi-Amiri and Nasseri in fuzzy Sets Syst 158:1961-1978, 2007) and Saati et al. (Saati et al. in Int J Inf Decis Sci 7:312-333, 2015), and clearly the advantages of the proposed method are discussed in conclusion section.

关键词： fuzzy optimization fuzzy linear programming problem fuzzy feasible solution fuzzy objective function Trapezoidal fuzzy number r-cut fuzzy centre fuzzy radius

来源：评论

学校读者我要写书评

暂无评论

Image clustering algorithm using superpixel segmentation and non-symmetric Gaussian-Cauchy mixture model

引用

IET IMAGE PROCESSING 2020年第16期14卷 4132-4143页

作者： Ji, Sifan Zhu, Hongqing Wang, Pengyu Ling, Xiaofeng East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this study, an unsupervised clustering algorithm is proposed to label superpixel density images. Firstly, the authors propose a novel superpixel segmentation algorithm driven by a modified fuzzy C-means objective function, Kullback-Leibler (KL) divergence, and an entropy term, which generate superpixels with good boundary adherence and intensity homogeneity. In this model, the logarithm of Gaussian distribution as a new distance metric is used to improve the accuracy of boundary pixel classification, the KL divergence is applied to regularise the fuzzy objective function. Based on this model, the generated superpixel intensity images with a highly distinctive background colour from the colour of the target are obtained. Grouping cues generated by superpixels can affect the performance of image clustering greatly. Next, according to the small amount of clustering data generated by the superpixel intensity images, they construct a non-symmetric mixture model based on a mixture of Gaussian distribution and Cauchy distribution for implementing image clustering. Thus, clustering of colour images is transformed into clustering of these newly generated data. The advantage of this model is its well adaption to different shapes of observed data. Experimental results on publicly available data sets are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： image colour analysis Gaussian distribution image segmentation pattern clustering mixture models image classification computer vision fuzzy set theory entropy unsupervised learning image clustering algorithm-based superpixel segmentation nonsymmetric Gaussian-Cauchy mixture model computer vision research unsupervised clustering algorithm superpixel density images novel superpixel segmentation algorithm Kullback-Leibler divergence good boundary adherence intensity homogeneity Gaussian distribution KL divergence fuzzy objective function generated superpixel intensity images clustering data nonsymmetric mixture model natural colour images newly generated data clustering model

来源：评论

学校读者我要写书评

暂无评论

ON SOLVING MULTI objective SET COVERING PROBLEM WITH IMPRECISE LINEAR FRACTIONAL objectiveS

引用

RAIRO-OPERATIONS RESEARCH 2015年第3期49卷 495-510页

作者： Upmanyu, Mudita Saxena, Ratnesh R. Univ Delhi Dept Math Delhi 110007 India Univ Delhi Deen Dayal Upadhyaya Coll Dept Math Delhi 110007 India

The Set Covering problem is one the of most important NP-complete 0-1 integer programming problems because it serves as a model for many real world problems like the crew scheduling problem, facility location problem, vehicle routing etc. In this paper, an algorithm is suggested to solve a multi objective Set Covering problem with fuzzy linear fractional functionals as the objectives. The algorithm obtains the complete set of efficient cover solutions for this problem. It is based on the cutting plane approach, but employs a more generalized and a much deeper form of the Dantzig cut. The fuzziness in the problem lies in the coefficients of the objective functions. In addition, the ordering between two fuzzy numbers is based on the possibility and necessity indices introduced by Dubois and Prade [Ranking fuzzy numbers in the setting of possibility theory. Inf. Sci. 30 (1983) 183-224.]. Our aim is to develop a method which provides the decision maker with a fuzzy solution. An illustrative numerical example is elaborated to clarify the theory and the solution algorithm.

关键词： Set covering Problem multi objective linear fractional programming fuzzy mathematical programming fuzzy objective function

来源：评论

学校读者我要写书评

暂无评论

Robust optimality analysis of non-degenerate basic feasible solutions in linear programming problems with fuzzy objective coefficients

引用

fuzzy OPTIMIZATION AND DECISION MAKING 2023年第1期22卷 51-79页

作者： Inuiguchi, Masahiro Gao, Zhenzhong Henriques, Carla Oliveira Osaka Univ Grad Sch Engn Sci 1-3 Machikaneyama Cho Toyonaka Osaka Japan Polytech Coimbra Coimbra Business Sch Res Ctr ISCAC P-3040316 Coimbra Portugal Univ Coimbra INESC Coimbra DEEC Polo 2 P-3030290 Coimbra Portugal Univ Coimbra Fac Econ CeBER Av Dias da Silva 165 P-3004512 Coimbra Portugal

The necessarily optimal solution is known as the most reasonable solution to linear programming problems with interval/fuzzy objective function coefficients. As it remains optimal against the certain fluctuations of objective function coefficients, the necessarily optimal solution can be seen as a robust optimal solution. In this paper, we demonstrate that the necessary optimality degree of a non-degenerate basic feasible solution can be obtained easily by utilizing the tolerance approach. The necessary optimality degree evaluates to what extent the solution remains optimal against the fluctuations of objective function coefficients. Several types of fuzzy subsets showing the possible range of the objective function coefficient vector are considered. For each type of fuzzy subset, an efficient calculation method of necessary optimality degree is proposed. Numerical examples are given to illustrate the proposed methods.

关键词： fuzzy linear programming fuzzy objective function Necessary optimality degree Oblique fuzzy vector Tolerance approach

来源：评论

学校读者我要写书评

暂无评论

On solving a multiobjective fixed charge problem with imprecise fractional objectives

引用

APPLIED SOFT COMPUTING 2016年 40卷 64-69页

作者： Upmanyu, M. Saxena, R. R. Univ Delhi Dept Math Delhi 110007 India Univ Delhi Dept Math Deen Dayal Upadhyaya Coll Delhi 110007 India

The fixed charge problem is a special type of nonlinear programming problem which forms the basis of many industry problems wherein a charge is associated with performing an activity. In real world situations, the information provided by the decision maker regarding the coefficients of the objective functions may not be of a precise nature. This paper aims to describe a solution algorithm for solving such a fixed charge problem having multiple fractional objective functions which are all of a fuzzy nature. The enumerative technique developed not only finds the set of efficient solutions but also a corresponding fuzzy solution, enabling the decision maker to operate in the range obtained. A real life numerical example in the context of the ship routing problem is presented to illustrate the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Fixed charge problem Multiobjective programming Fractional programming fuzzy objective function

来源：评论

学校读者我要写书评

暂无评论

Robust estimation of discrete hidden Markov model parameters using the entropy-based feature-parameter weighting and source-quantization modeling

引用

ARTIFICIAL INTELLIGENCE IN ENGINEERING 1998年第3期12卷 243-252页

作者： Choi, HJ Yun, SJ Oh, YH Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea

We propose a new variant of the discrete hidden Markov model (DHMM) in which the output distribution is estimated by state-dependent source quantizing modeling and the output probability is weighted by the entropy of each feature-parameter at a state. The state-dependent source is represented as a state-dependent quantized vector which is regarded as a variant of a representative vector at a state and its own codeword distribution, and the output distribution is derived by these state-dependent sources which will exist at a state. In addition, entropy-based feature-parameter weighting is proposed to reflect the different importance of each feature-parameter in a state, and the fuzzy function is applied to transform an entropy value into a feature-parameter weighting factor. From experiments, we found that proposed methods have shown an improvement of 5.6%, which indicates the effectiveness of proposed models in the robust estimation of output probabilities for DHMMs. (C) 1998 Elsevier Science Limited. All rights reserved.

关键词： hidden Markov model state-dependent source quantization modeling entropy feature-parameter weighting fuzzy objective function

来源：评论

学校读者我要写书评

暂无评论

Robust image segmentation using genetic algorithm with a fuzzy measure

引用

PATTERN RECOGNITION 1996年第7期29卷 1195-1211页

作者： Chun, DN Yang, HS ELECTR & TELECOMMUN RES INST MEDIA TECHNOL SECTYUSONG GUTAEJON 305600SOUTH KOREA KOREA ADV INST SCI & TECHNOL DEPT COMP SCICTR ARTIFICIAL INTELLIGENCE RESYUSONG GUTAEJON 305701SOUTH KOREA

In this paper we present new region-based image segmentation methodology on gray-level images using a genetic algorithm with a fuzzy measure. We first propose a fuzzy validity function which measures a degree of separation and compactness between and within finely segmented regions, and an edge strength along boundaries of all regions. We apply the generic algorithm to search a good or usable region segmentation, which maximizes the quality of regions generated by split- and-merge processing. The iterative algorithm provides a useful method for image segmentation without the need for critical parameters or threshold values, iterative visual interaction or a priori knowledge of an image. Copyright (C) 1996 Pattern Recognition Society.

关键词： genetic algorithm split-and-merge image segmentation validity measurement fuzzy objective function

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：