انجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Neural networks in analyzing fuzzy information of OCTشبکه های عصبی در تحلیل اطلاعات فازی از تصاویر شبکیه چشم12014607610.22034/jfsa.2022.315462.1101FAحامدفهیمیدانشگاه شهید چمران اهواز، دانشکده علوم ریاضی و کامپیوتر، گروه علوم کامپیوترجلالچاچیدانشگاه شهید چمران اهواز، دانشکده علوم ریاضی و کامپیوتر، گروه آمار0000-0003-1883-5113احمدکاظمی فرددانشگاه شهید چمران اهواز، دانشکده علوم ریاضی و کامپیوتر، گروه ریاضیJournal Article20211116In this regard, in the framework of deep learning and based on inaccurate information obtained from images of retinal layers, an end-to-end network has been trained to analyze and segment retinal layers. The result of this study is the introduction of a kit that can be installed on smartphones, which can be used to easily and simply monitor users' vision.<br />The input of this structure is OCT images of the eye, without the need to extract hand-crafted features as input from the image, and its output is accurate spatial information of the retinal layers. The time required for each scan of OCT input images in this kit is about 10ms, which makes it possible to use such kits in a variety of smartphones easily. This kit, which assesses the health of ocular arteries, is inexpensive and can be used for remote locations or people who are not able to visit specialized eye centers or need frequent and continuous monitoring of their eye health. The ranking results presented in the present article show that the proposed structure has an acceptable performance and is very close to the average opinions of the group of specialist ophthalmologists. The results of evaluations and comparisons are made within the framework of multi-criteria decision-making approach. In order to extract the results in this framework, the TOPSIS algorithm is used in which the weights of the indicators are determined based on the entropy size.در اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺗﺤﻠﯿﻞ اﻃﻼﻋﺎت ﻧﺎدﻗﯿﻖ از ﺗﺼﺎوﯾﺮ ﺷﺒﮑﯿﻪ ﭼﺸﻢ اﻧﺴﺎن در ﻗﺎﻟﺐ ﯾﮏ ﮐﯿﺖ ﻗﺎﺑﻞ ﻧﺼﺐ ﺑﺮ ﮔﻮﺷﯽﻫﺎی ﻫﻮﺷﻤﻨﺪ ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. در اﯾﻦﺧﺼﻮص اﯾﻦ ﻣﻨﻈﻮر در ﭼﺎرﭼﻮب ﯾﺎدﮔﯿﺮی ﻋﻤﯿﻖ و ﺑﺮ اﺳﺎس اﻃﻼﻋﺎت ﻧﺎدﻗﯿﻖ ﺣﺎﺻﻞ از ﺗﺼﺎوﯾﺮ ﻻﯾﻪﻫﺎی ﺷﺒﮑﯿﻪ ﺑﻪ آﻣﻮزش ﯾﮏ ﺷﺒﮑﻪ اﻧﺘﻬﺎ-ﺑﻪ-اﻧﺘﻬﺎ ﺑﻪ ﻣﻨﻈﻮر ﺗﺤﻠﯿﻞ و ﻗﻄﻌﻪﺑﻨﺪی ﻻﯾﻪﻫﺎی ﺷﺒﮑﯿﻪ ﻣﺒﺎدرت ﺷﺪه اﺳﺖ. ﻧﺘﯿﺠﮥ اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﻪ ﻣﻌﺮﻓﯽ ﯾﮏ ﮐﯿﺖ ﻗﺎﺑﻞ ﻧﺼﺐ ﺑﺮ ﮔﻮﺷﯽﻫﺎی ﻫﻮﺷﻤﻨﺪ ﺧﺘﻢ ﻣﯽﺷﻮد ﮐﻪ ﺑﺎ اﺳﺘﻔﺎده از آن ﺑﻪ<br />راﺣﺘﯽ و ﺑﻪ آﺳﺎﻧﯽ ﻣﯽﺗﻮان ﺑﺮ ﺑﯿﻨﺎﯾﯽ ﮐﺎرﺑﺮان ﻧﻈﺎرت داﺷﺖ.<br /> https://jfsa.fuzzy.ir/article_146076_221fec1d54a07a65c1ff500bcf1bc062.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Application of trapezoidal fuzzy quality in the automobile manufacturing industryکاربرد کیفیت فازی ذوزنقه ای در صنعت خودروسازی214214607710.22034/jfsa.2022.310751.1095FAحمیدهایرانمنشﮔﺮوه آﻣﺎر، داﻧﺸﮑﺪه ﻋﻠﻮم رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﻓﺮدوﺳﯽ ﻣﺸﻬﺪ، ﻣﺸﻬﺪ، اﯾﺮان0000-0002-2178-8461عباسپرچمیﮔﺮوه آﻣﺎر، داﻧﺸﮑﺪه رﯾﺎﺿﯽ و راﯾﺎﻧﻪ، داﻧﺸﮕﺎه ﺷﻬﯿﺪ ﺑﺎﻫﻨﺮ ﮐﺮﻣﺎن، ﮐﺮﻣﺎن، اﯾﺮان0000-0002-0593-7324مهدیجباری نوقابیﮔﺮوه آﻣﺎر، داﻧﺸﮑﺪه ﻋﻠﻮم رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﻓﺮدوﺳﯽ ﻣﺸﻬﺪ، ﻣﺸﻬﺪ، اﯾﺮان0000-0002-5636-2209Journal Article20211017Hypotheses testing is an effective technique for decision making on the manufacturing process capability. Considering fuzzy quality rather than precise specification limits, we can make more reliable decisions for investigating the manufacturing process capability. In this paper, an applied study on the basis of fuzzy quality using the Yongting’s index is presented. The proposed approach which is used in this applied study is a technique for testing the capability of a normal process in producing products within the preset fuzzy specification limits. The adversity to test process capability indices is complexity in the distribution of its natural estimator, even under the Normal distribution. Also, there is the challenge in testing process capability based on fuzzy quality. The non-parametric approach which is used to test the performance of a product based on fuzzy quality and random sampling techniques which is presented based on the Monte Carlo simulation method and can be generalized for various fuzzy qualities. This study is presented to investigate the quality of paint thickness in the automobile polishing process based on trapezoidal fuzzy quality. The numerical computations are presented to show the performance of the Monte Carlo simulation method for making reliable decisions in testing the Yongting’s index.آزﻣﻮن ﻓﺮﺿﯿﻪ آﻣﺎری ﯾﮏ روش ﻣﻮﺛﺮ ﺑﺮای ﺗﺼﻤﯿﻢﮔﯿﺮی در ﻣﻮرد ﮐﺎراﯾﯽ ﯾﮏ ﻓﺮاﯾﻨﺪ ﺗﻮﻟﯿﺪی ﻣﯽﺑﺎﺷﺪ. ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﮐﯿﻔﯿﺖ ﻓﺎزی ﺑﻪ ﺟﺎی ﺣﺪود ﻣﺸﺨﺼﺎت ﻓﻨﯽ دﻗﯿﻖ، ﻣﯽﺗﻮاﻧﯿﻢ ﺗﺼﻤﯿﻤﺎت ﻣﻄﻤﺌﻦﺗﺮی ﺑﺮای ﺑﺮرﺳﯽ ﺗﻮاﻧﺎﯾﯽ ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪﻫﺎی ﺗﻮﻟﯿﺪی ﺑﮕﯿﺮﯾﻢ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﯾﮏ ﻣﻄﺎﻟﻌﻪ ﮐﺎرﺑﺮدی ﺑﺮ اﺳﺎس ﮐﯿﻔﯿﺖ ﻓﺎزی ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﯾﺎﻧﮕﺘﯿﻨﮓ اراﺋﻪ ﺷﺪه اﺳﺖ. روﯾﮑﺮد ﭘﯿﺸﻨﻬﺎدی ﺑﻪ ﮐﺎر ﺑﺮدهﺷﺪه در اﯾﻦ ﻣﻄﺎﻟﻌﮥ ﮐﺎرﺑﺮدی، ﯾﮏ ﺗﮑﻨﯿﮏ ﺑﺮای آزﻣﻮدن ﺗﻮاﻧﺎﯾﯽ ﯾﮏ ﻓﺮاﯾﻨﺪ ﻧﺮﻣﺎل در ﺗﻮﻟﯿﺪ ﻣﺤﺼﻮﻻت در ﺣﺪود ﻣﺸﺨﺼﺎت ﻓﺎزی از ﭘﯿﺶﺗﻌﯿﯿﻦﺷﺪه ﻣﯽﺑﺎﺷﺪ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﭘﯿﭽﯿﺪﮔﯽ ﻓﺮﻣﻮلﻫﺎی ﺷﺎﺧﺺﻫﺎی ﮐﺎراﯾﯽ ﺣﺘﯽ ﺗﺤﺖ ﺷﺮاﯾﻂ ﻧﺮﻣﺎل ﺑﻮدن دادهﻫﺎ، ﻣﻤﮑﻦ اﺳﺖ ﺑﺎ ﭼﺎﻟﺶ ﻋﺪم ﺗﻮاﻧﺎﯾﯽ ﭘﯿﺪا ﮐﺮدن ﺗﻮزﯾﻊ آﻣﺎری ﺑﺮآوردﮔﺮ ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪ روﺑﺮو ﺷﻮﯾﻢ. ﻫﻤﭽﻨﯿﻦ اﯾﻦ ﭼﺎﻟﺶ ﻧﯿﺰ ﺑﺮای آزﻣﻮن ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪ ﺑﺮ اﺳﺎس ﮐﯿﻔﯿﺖ ﻓﺎزی دﯾﺪه ﻣﯽﺷﻮد.https://jfsa.fuzzy.ir/article_146077_2ed67ae59bb1fc81da3b05f78960c0c5.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Time-Cost-Quality Trade-off in Project Scheduling with fuzzy qualityموازنه هزینه، زمان، کیفیت در زمانبندی پروژه با کیفیت فازی436814607810.22034/jfsa.2022.317490.1105FAناصرشهسواری پورداﻧﺸﮑﺪه اﻗﺘﺼﺎد و ﻣﺪﯾﺮﯾﺖ داﻧﺸﮕﺎه وﻟﯽ ﻋﺼﺮ(ﻋﺞ) رﻓﺴﻨﺠﺎن. اﯾﺮان0000-0002-8671-7672شهلاحیدربیگیﻣﻮﺳﺴﻪ ﻋﺎﻟﯽ آﻣﻮزش ﭘﮋوﻫﺶ ﻣﺪﯾﺮﯾﺖ و ﺑﺮﻧﺎﻣﻪ رﯾﺰی. اﯾﺮان0000-0002-4399-6106Journal Article20211129Project managers often face different and sometimes conflicting goals in optimizing project resources. In recent years, the demand of the project stakeholders to reduce the total cost of the project and reduce the time and increase its quality have intensified. Therefore, the balance of time, cost, and quality are the project's main goals. These topics lead researchers to develop models that add a quality factor to previous time balance models. This research provides a model for balancing cost, time and, quality, in discrete mode. Of course, since the quality factor is not a quantitative factor-like cost and time factors, and in the real world of projects, quality is a linguistic variable. It is obtained from the point of view of experts, so in this research, the quality of activities is shown as fuzzy numbers. By finding the optimal combination (time, cost, quality) of each activity, this model compresses the activities to reduce the costs of the whole project, while achieving the desired quality. A new metaheuristic algorithm called NHGA is introduced to solve the model, which has a much higher efficiency than GA to solve model, so with a case study, the efficiency of the proposed algorithm and the flexibility of the proposed model for project managers is shownﻣﺪﯾﺮان ﭘ ﺮوژه اﻏﻠﺐ ﺑ ﺎ اﻫﺪاف ﻣﺨﺘﻠﻒ و ﮔ ﺎﻫﺎً ﻣﺘﻀ ﺎد در ﺑ ﻬﯿﻨﻪﺳ ﺎزی ﻣﻨ ﺎﺑ ﻊ ﭘﺮوژهﻫﺎ روﺑﺮو ﻫﺴﺘﻨﺪ. در ﺳﺎلﻫﺎی اﺧﯿﺮ ﻧﯿﺰ ﺗﻘﺎﺿﺎی ذﯾﻨﻔﻌﺎن ﭘﺮوژه، ﻣﺒﻨﯽ ﺑﺮ ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪﻫﺎی ﮐﻞ ﭘﺮوژه ﻫﻢزﻣﺎن ﺑﺎ ﮐﺎﻫﺶ ﻣﺪتزﻣﺎن و اﻓﺰاﯾﺶ ﮐﯿﻔﯿﺖ آن ﺗﺸﺪﯾﺪ ﺷﺪه اﺳﺖ. ﻟﺬا ﻣﻮازﻧﻪ زﻣﺎن، ﻫﺰﯾﻨﻪ و ﮐﯿﻔﯿﺖ از اﻫﺪاف اﺳﺎﺳﯽ ﭘ ﺮوژه ﻣﺤﺴﻮب ﻣﯽﺷﻮﻧﺪ. اﯾﻦ ﻣﻮﺿﻮﻋﺎت ﭘﮋوﻫﺸﮕﺮان را ﺑﻪﺳﻮی ﺗﻮﺳﻌﻪ ﻣﺪلﻫﺎﯾﯽ ﮐﻪ ﻋﺎﻣﻞ ﮐﯿﻔﯿﺖ را ﺑﻪ ﻣﺪلﻫﺎی ﻗﺒﻠﯽ ﻣﻮازﻧﻪ ﻫﺰﯾﻨﻪ زﻣﺎن ﻣﯽاﻓﺰاﯾﺪ، ﻫﺪاﯾﺖ ﻣﯽﮐﻨﺪ. اﯾﻦ ﭘﮋوﻫﺶ ﻣﺪﻟﯽ ﺑﺮای ﻣﻮازﻧﻪ ﻫﺰﯾﻨﻪ، زﻣﺎن، ﮐﯿﻔﯿﺖ، در ﺣﺎﻟﺖ ﮔﺴﺴﺘﻪ اراﺋﻪ ﻣﯽدﻫﺪ. اﻟﺒﺘﻪ ازآﻧﺠﺎﮐﻪ ﻋﺎﻣﻞ ﮐﯿﻔﯿﺖ ﻣﺎﻧﻨﺪ ﻋﻮاﻣﻞ ﻫﺰﯾﻨﻪ و زﻣﺎن ﯾﮏ ﻋﺎﻣﻞ ﮐﻤﯽ ﻧﯿﺴﺖ و در دﻧﯿ ﺎی واﻗ ﻌﯽ ﭘ ﺮوژهﻫﺎ، ﮐﯿﻔﯿﺖ ﯾﮏ ﻣﺘﻐﯿ ﺮ زﺑﺎﻧﯽ اﺳ ﺖ و ازﻧﻈﺮ اﺷﺨﺎص ﺧﺒﺮه ﺑﻪ دﺳﺖ ﻣﯽآﯾﺪ.https://jfsa.fuzzy.ir/article_146078_9357afc7f736ada70e68c8e9704543ef.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220A neurodynamic model for solving nonsmooth constrained optimization problems with affine and bound constraintsیک مدل عصبی پویای کارا برای حل مسایل بهینه سازی ناهموار مقید با محدودیت‌های آفین و کران‌دار6911214608110.22034/jfsa.2022.316313.1103FAمحمد جوادعبادیگروه ریاضی، دانشگاه دریانوردی و علوم دریایی چابهار، چابهار، ایران0000-0002-1324-6953حامدفراهانیگروه ریاضی، دانشگاه دریانوردی و علوم دریایی چابهار، چابهار، ایرانحسینجعفریگروه ریاضی، دانشگاه دریانوردی و علوم دریایی چابهار، چابهار، ایرانJournal Article20211122In this paper, a non-penalty differential inclusion-based model is proposed for solving nonsmooth convex optimization problems subject to linear equality and bound constraints. We prove the convergence of the trajectories to the equality feasible region in finite time. Also, the equivalence of the equilibrium point of the proposed neural network and the optimal solution of the original optimization problem is shown. In addition, we prove the stability of the proposed neural network in the sense of Lyapunov and the global convergence to an exact optimal solution to the original problem. In comparison with some existing models for solving nonsmooth convex optimization problems, there does not exist any penalty parameter or penalty function in the model's structure and the implementation of the proposed model is easier. In the end, as an application, the proposed neural network is reduced to a model for solving nonsmooth convex optimization problems subject to linear equality and $x\geq0$ constraints. Also, some illustrative examples are given to show the effectiveness of the proposed neural networks.در اﯾﻦ ﻣﻘﺎﻟﻪ ﯾﮏ ﻣﺪل ﻏﯿﺮﺟﺮﯾﻤﻪای ﻣﺒﺘﻨﯽ ﺑﺮ ﺷﻤﻮل دﯾﻔﺮاﻧﺴﯿﻠﯽ ﺑﺮای ﺣﻞ ﻣﺴﺎﯾﻞ ﺑﻬﯿﻨﻪﺳﺎزی ﻣﻘﯿﺪ ﺑﺎ ﻗﯿﻮد ﮐﺮاندار و ﺗﺴﺎوی ﺧﻄﯽ ﭘﯿﺸﻨﻬﺎد ﮔﺮدﯾﺪه اﺳﺖ. ﻫﻤﮕﺮاﯾﯽ ﺧﻂ ﺳﯿﺮﻫﺎ ﺑﻪ ﻧﺎﺣﯿﻪی ﺷﺪﻧﯽ ﻣﺴﺎوی در زﻣﺎن ﻣﺘﻨﺎﻫﯽ را اﺛﺒﺎت ﻧﻤﻮدهاﯾﻢ. ﻫﻢﭼﻨﯿﻦ ﻣﻌﺎدل ﺑﻮدن ﻧﻘﻄﻪ ﺗﻌﺎدﻟﯽ ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﭘﯿﺸﻨﻬﺎدی ﺑﺎ ﺟﻮاب ﺑﻬﯿﻨﻪی ﻣﺴﺄﻟﻪ ﺑﻬﯿﻨﻪﺳﺎزی اﺻﻠﯽ را ﻧﺸﺎن دادهاﯾﻢ. ﺑﻪﻋ ﻼوه ﭘﺎﯾﺪاری ﺷﺒﮑﻪی ﭘﯿﺸﻨﻬﺎدی ﺑﻪ ﻣﻔﻬﻮم ﻟﯿﺎﭘﺎﻧﻮف و ﻫﻤﮕﺮاﯾﯽ ﺳﺮاﺳﺮی آن ﺑﻪ ﺟﻮاب ﺑﻬﯿﻨﻪی دﻗﯿﻖ ﻣﺴﺄﻟﻪ ﺑﻬﯿﻨﻪﺳﺎزی اﺻﻠﯽ اﺛﺒﺎت ﮔﺮدﯾﺪه اﺳﺖ. ﻣﺪل ﭘﯿﺸﻨﻬﺎدی در ﻣﻘﺎﯾﺴﻪ ﺑﺎ ﻣﺪلﻫﺎی ﻣﻮﺟﻮد ﺑﺮای ﺣﻞ ﻣﺴﺎﯾﻞ ﺑﻬﯿﻨﻪﺳﺎزی ﻣﺤﺪب ﻧﺎﻫﻤﻮار ﻓﺎﻗﺪ ﭘﺎراﻣﺘﺮﺟﺮﯾﻤﻪ ﯾﺎ ﺗﺎﺑﻊ ﺟﺮﯾﻤﻪ ﺑﻮده و ﭘﯿﺎدهﺳﺎزی آن آﺳﺎنﺗﺮ ﻣﯽﺑﺎﺷﺪ. ﺑﻪﻋﻨﻮان ﮐﺎرﺑﺮد، ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﭘﯿﺸﻨﻬﺎدی ﺑﻪ ﯾﮏ ﻣﺪل ﺑﺮای ﺣﻞ ﻣﺴﺎﯾﻞ ﺑﻬﯿﻨﻪﺳﺎزی ﻣﺤﺪب ﻧﺎﻫﻤﻮار ﻣﻘﯿﺪ ﺑﺎ ﻗﯿﻮد ﻣﺴﺎوی ﺧﻄﯽ و ٠<em> </em><em>≥</em><em> </em><em>x</em> ﺗﺒﺪﯾﻞ ﺷﺪه اﺳﺖ. در اﻧﺘﻬﺎ ﺑﺮای ﻧﺸﺎن دادن ﮐﺎراﯾﯽ ﻣﺪل ﭘﯿﺸﻨﻬﺎدی ﺗﻌﺪادی ﻣﺜﺎل اراﯾﻪ ﮔﺮدﯾﺪه اﺳﺖ.https://jfsa.fuzzy.ir/article_146081_5701e958d205b391b057f455e53d757c.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Relation Between Deterministic Fuzzy Tree Automata and Normal Recognizable Step Mappingsارتباط بین اتوماتای درختی فازی قطعی و نگاشت های مرحله ای تشخیص پذیر نرمال11313214607910.22034/jfsa.2022.315340.1100FAمریمقرآنیداﻧﺸﮑﺪه ﻋﻠﻮم رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﺻﻨﻌﺘﯽ ﺷﺎﻫﺮود، ﺷﺎﻫﺮود، اﯾﺮانJournal Article20211115In this paper we investigate fuzzy tree automata. Fuzzy tree automata extend fuzzy automata. In fuzzy automata we deal with strings and in fuzzy tree automata we utilize trees instead of strings. We define a recognizable step fuzzy tree language and by a theorem we show that recognizable fuzzy tree language is recognizable step fuzzy tree language. Then, we define a deterministic fuzzy tree automaton and support of a fuzzy tree language and show that support of a fuzzy tree language can be recognized by a fuzzy tree automaton. We explain these concepts by examples. We also show that for a recognizable step mapping, there exists a deterministic fuzzy tree automaton that can recognize it, and vice versa. In fact we prove that the class of recognizable fuzzy tree languages is equal to the class of normal recognizable step mappings.The obtained theorems generalize the results of weighted automata to tree automata.در اﯾﻦ ﻣﻘﺎﻟﻪ، ﺑﻪ ﺑﺮرﺳﯽ اﺗﻮﻣﺎﺗﺎی درﺧﺘﯽ ﻓﺎزی ﻣﯽﭘﺮدازﯾﻢ. ﯾﮏ زﺑﺎن درﺧﺖ ﻓﺎزی ﻣﺮﺣﻠﻪای ﺗﺸﺨﯿﺺﭘﺬﯾﺮ را ﺗﻌﺮﯾﻒ ﮐﺮده و ﻧﺸﺎن ﻣﯽدﻫﯿﻢ ﻫﺮ زﺑﺎن درﺧﺖ ﻓﺎزی ﺗﺸﺨﯿﺺﭘﺬﯾﺮ ﯾﮏ زﺑﺎن درﺧﺖ ﻓﺎزی ﻣﺮﺣﻠﻪای ﺗﺸﺨﯿﺺﭘﺬﯾﺮ اﺳﺖ. در اداﻣ ﻪ، ﯾ ﮏ اﺗ ﻮﻣ ﺎﺗ ﻮن درﺧﺘ ﯽ ﻓ ﺎزی ﻗ ﻄ ﻌ ﯽ را ﻣ ﻌ ﺮﻓ ﯽ ﮐ ﺮده و ﻧﺸ ﺎن ﻣ ﯽدﻫﯿ ﻢ ﺗﮑﯿ ﻪﮔ ﺎه ﯾﮏ زﺑﺎن درﺧﺖ ﻓ ﺎزی ﻗﺎﺑﻞ ﺗﺸﺨﯿﺺ ﺗ ﻮﺳﻂ ﯾﮏ اﺗ ﻮﻣ ﺎﺗﻮن درﺧﺘﯽ ﻓﺎزی، ﺗﺸﺨﯿﺺﭘﺬﯾﺮ اﺳﺖ. اﯾﻦ ﻣﻔﺎﻫﯿﻢ را روی ﺑﺮﺧﯽ اﺗﻮﻣﺎﺗﺎی درﺧﺘﯽ ﻓﺎزی ﻧﻤﺎﯾﺶ ﻣﯽدﻫﯿﻢ. ﻫﻤﭽﻨﯿﻦ، ﺛﺎﺑﺖ ﻣﯽﮐﻨﯿﻢ ﮐﻪ ﮐﻼس زﺑﺎنﻫﺎی درﺧﺖ ﻓﺎزی ﻗﺎﺑﻞ ﺗﺸﺨﯿﺺ ﺗﻮﺳﻂ اﺗﻮﻣﺎﺗﺎی درﺧﺘﯽ ﻓﺎزی ﻗﻄﻌﯽ، ﺑﺎ ﮐﻼس ﻧﮕﺎﺷﺖﻫﺎی ﻣﺮﺣﻠﻪای ﺗﺸﺨﯿﺺﭘﺬﯾﺮ ﺑﺮاﺑﺮ اﺳﺖ. در ﻧﻬﺎﯾﺖ، ﻧﺸﺎن ﻣﯽدﻫﯿﻢ ﮐﻪ ﯾﮏ زﺑﺎن درﺧﺖ ﻓﺎزی ﺗﻮﺳﻂ ﯾﮏ اﺗﻮﻣﺎﺗﻮن درﺧﺘﯽ ﻓﺎزی ﻗﻄﻌﯽ ﻗﺎﺑﻞ ﺗﺸﺨﯿﺺ اﺳﺖ اﮔﺮ و ﻓﻘﻂ اﮔﺮ آن زﺑﺎن، ﯾﮏ ﻧﮕﺎﺷﺖ ﻣﺮﺣﻠﻪای ﺗﺸﺨﯿﺺﭘﺬﯾﺮ ﻧﺮﻣﺎل ﺑﺎﺷﺪ.https://jfsa.fuzzy.ir/article_146079_c56f51881d1e46a85a70147d8f2bdd72.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Theهوش چیست و هوشمند کیست؟ (به سوی تعریف فراگیر و یکتا برای هوش)13315014608010.22034/jfsa.2022.318262.1106FAسید محمودطاهریداﻧﺸﮑﺪۀ ﻓﻨﯽ، داﻧﺸﮕﺎه تهرانJournal Article20211204Can intelligence be defined comprehensively and uniquely? Basically what is intelligence and<br />Who is smart? The definition of intelligence in psychology and related general, one of the foundations of theorizing in<br />Analysis of human behavior. On the other hand, the definition of intelligence is the theoretical basis in design and<br />Build intelligent systems. In fact, with any definition of intelligence, to the intelligent system<br />We will arrive differently. In this article, based on the definitions provided by two categories of scientists<br />(Psychologists and intelligent systems specialists), identify the main components of intelligence<br />we do. Then, based on these components, we divide the types of definitions of intelligence. Then<br />We strive to provide a unique and comprehensive definition of intelligence and intelligence so that all aspects<br />Include intelligence. We will see that this definition is closely related to the concept of uncertainty.<br />Analysts thus clarify the connection between intelligence and uncertainty, and thus the way<br />Human behavior and intelligent systems specialists become smoother. Can intelligence be defined in a comprehensive and unique way? Basically what is intelligence and<br />Who is smart? The definition of intelligence in psychology and related general, one of the foundations of theorizing in<br />Analysis of human behavior. On the other hand, the definition of intelligence is the theoretical basis in design and<br />Build intelligent systems. In fact, with any definition of intelligence, to the intelligent system<br />We will arrive differently.آﯾﺎ ﻣﯽﺗﻮان ﻫﻮش را ﺑﻪﺻﻮرت ﻓﺮاﮔﯿﺮ و ﯾﮏﭘﺎرﭼﻪ ﺗﻌﺮﯾﻒ ﮐﺮد؟ اﺳﺎﺳﺎً ﻫﻮش ﭼﯿﺴﺖ و ﻫﻮﺷﻤﻨﺪ ﮐﯿﺴﺖ؟ ﺗﻌﺮﯾﻒ ﻫﻮش در روانﺷﻨﺎﺳﯽ و ﻋﻠﻮم ﻣﺮﺗﺒﻂ، ﯾﮑﯽ از ﻣﺒﺎﻧﯽ ﻧﻈﺮﯾﻪﭘﺮدازی در ﺗﺤﻠﯿﻞ رﻓﺘﺎر اﻧﺴﺎنﻫﺎ اﺳﺖ. از ﺳﻮی دﯾﮕﺮ، ﺗﻌﺮﯾﻒ ﻫﻮﺷﻤﻨﺪی ﻣﺒﻨﺎی ﻧﻈﺮی در ﻃﺮاﺣﯽ و ﺳﺎﺧﺖ ﺳﯿﺴﺘﻢﻫﺎی ﻫﻮﺷﻤﻨﺪ اﺳﺖ. در واﻗﻊ، ﺑﺎ ﻫﺮ ﺗﻌﺮﯾﻔﯽ از ﻫﻮش، ﺑﻪ ﺳﯿﺴﺘﻢ ﻫﻮﺷﻤﻨﺪ ﻣﺘﻔﺎوﺗﯽ ﺧﻮاﻫﯿﻢ رﺳﯿﺪ. در اﯾﻦ ﻣﻘﺎﻟﻪ، ﺑﺮﭘﺎﯾﮥ ﺗﻌﺮﯾﻒﻫﺎی دو رده از داﻧﺸﻤﻨﺪان )روانﺷﻨﺎﺳﺎن و ﻣﺘﺨﺼﺼﺎن ﺳﯿﺴﺘﻢﻫﺎی ﻫﻮﺷﻤﻨﺪ(، ﻣﺆﻟﻔﻪﻫﺎی اﺻﻠﯽ ﻫﻮش را ﺑﺎزﺷﻨﺎﺳﺎﯾﯽ ﻣﯽﮐﻨﯿﻢ. ﺳﭙﺲ ﺑﺮاﺳﺎس اﯾﻦ ﻣﺆﻟﻔﻪﻫﺎ، ﺑﻪ ﺗﻘﺴﯿﻢﺑﻨﺪی اﻧﻮاع ﺗﻌﺮﯾﻒﻫﺎی ﻫﻮش ﻣﯽﭘﺮدازﯾﻢ. آنﮔﺎه ﺗﻼش ﻣﯽﮐﻨﯿﻢ ﺗﺎ ﺗﻌﺮﯾﻔﯽ ﻓﺮاﮔﯿﺮ و ﯾﮏﭘﺎرﭼﻪ درﺑﺎرۀ ﻫﻮش و ﻫﻮﺷﻤﻨﺪی اراﺋﻪ ﮐﻨﯿﻢ ﻃﻮریﮐﻪ ﻫﻤﮥ ﺟﻮاﻧﺐ ﻫﻮش را در ﺑﺮ داﺷﺘﻪ ﺑﺎﺷﺪ. ﺧﻮاﻫﯿﻢ دﯾﺪ ﮐﻪ اﯾﻦ ﺗﻌﺮﯾﻒ ﭘﯿﻮﻧﺪی ژرف ﺑﺎ ﻣﻔﻬﻮم ﻧﺎﯾﻘﯿﻨﯽ دارد. ﺑﺪﯾﻦﺗﺮﺗﯿﺐ ارﺗﺒﺎط ﺑﯿﻦ ﻫﻮﺷﻤﻨﺪی و ﻧﺎﯾﻘﯿﻨﯽ آﺷﮑﺎرﺗﺮ ﻣﯽﺷﻮد، و راه ﭘﯿﺶروی ﺗﺤﻠﯿﻞﮔﺮان رﻓﺘﺎر اﻧﺴﺎﻧﯽ و ﻣﺘﺨﺼﺼﺎن ﺳﯿﺴﺘﻢﻫﺎی ﻫﻮﺷﻤﻨﺪ، روﺷﻦﺗﺮ ﻣﯽﮔﺮدد.https://jfsa.fuzzy.ir/article_146080_0ec52b1e1eac533c53d3d855486809d2.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Optimal design of electric machine used in spacecraft by using fuzzy multi-objective particle swarm algorithmطراحی بهینه ماشین الکتریکی استفاده شده در فضا پیماها با استفاده از الگوریتم ازدحام ذرات چند هدفه فازی15116414608210.22034/jfsa.2022.315912.1102FAعلیرضاحسین پورداﻧﺸﮑﺪه ﻓﻨﯽ و ﻣﻬﻨﺪﺳﯽ، داﻧﺸﮕﺎه زاﺑﻞ، زاﺑﻞ، اﯾﺮانمحموداوکاتی صادقداﻧﺸﮑﺪه ﺑﺮق و ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانJournal Article20211120Permanent magnet synchronous machines (PMSM) used in spacecraft must be able to control the magnet flux. For this purpose, in addition to the magnet, an excitation coil is placed on their rotor, which is widely used in control purposes. Reducing torque ripple and increasing the average torque are two important goals in the design of electric machines. In this paper, first the average torque and torque ripple for the PMSM in several cases are determined by numerical method. Then, analytical functions are obtained for them by using multilayer perceptron neural network. Fixed coefficients in the neural network are adjusted so that not only the errors for the training, test, validation, and total data, are very small but also the error for the test data is very close to the error for the training data. After that, by multi-objective fuzzy particle swarm optimization algorithm, the dimensions of the PMSM are determined in such a way that the torque ripple is minimum and the average value of torque is maximum. The determination of the optimal point at the pareto front will be determined by using the fuzzy maximum-minimum method. In this paper, numerical analysis is performed with Maxwell software and neural network, optimization and fuzzy are modeled by Matlab one.ماشینهای آهنربای دائمی که در فضاپیماها استفاده میشوند باید از قابلیت کنترل شار آهنربا برخوردار باشند. برای همین منظور بر روی روتور آنها علاوه بر آهنربا سیم پیچی تحریک هم قرار می گیرد که در اهداف کنترلی کاربرد زیادی دارد. کاهش نوسانات (ریپل) گشتاور و افزایش مقدار متوسط گشتاور دو هدف مهم در طراحی ماشینهای الکتریکی می باشند. در این مقاله ابتدا گشتاور متوسط و ریپل گشتاور برای ماشین مورد نظر در چند حالت با استفاده از روشهای عددی تعیین میشود. سپس برای آنها توابع تحلیلی با استفاده از شبکه عصبی پرسپترون چند لایه بدست می آید. ضرایب ثابت در شبکه عصبی ها طوری تنطیم میشوند که نه تنها خطا به داده های آموزش، آزمایش، اعتبارسنجی و کل داده ها بسیار کم باشد بلکه خطا برای داده های آزمایش با خطا برای داده های آموزش بسیار نزدیک باشد. بعد از آن با الگوریتم چند هدفه ازدحام ذرات فازی ابعاد ماشین به گونه ای تعیین میگردد که ریپل گشتاور مینیمم و مقدار متوسط گشتاور ماکزیمم شود. تعیین نقطه بهینه در سطح پرتو با استفاده از روش ماکزیمم-مینیمم فازی تعیین خواهد شد. در این مقاله تحلیل عددی با نرم افزار Maxwell و محاسبات شبکه عصبی، بهینه سازی و فازی با Matlab انجام میگردد.https://jfsa.fuzzy.ir/article_146082_f9eb01a2365d91214a80f881005ef0a9.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Fuzzy resource allocation in two-stage systemsتخصیص منابع فازی در سیستم های دومرحله ای16518514608310.22034/jfsa.2022.296256.1080FAفرانکحسینزاده سلجوقیداﻧﺸﮑﺪه رﯾﺎﺿﯽ آﻣﺎر و ﻋﻠﻮم ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانطیبهدهقانیداﻧﺸﮑﺪه رﯾﺎﺿﯽ آﻣﺎر و ﻋﻠﻮم ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانJournal Article20210722Organizations and institutions have internal departments that the overall performance of the organization is the result of the performance of each of these departments or stages. Each stage has its own input and output factors as well as connecting factors between stages. The factors between stages are called intermediate indicators. In a two-stage structure, the intermediate indicators are the outputs of the first stage, which are used as the input of the second stage. Evaluation performance of an organization should be determined by considering the performance of each of its stages. Data envelopment analysis is one of the appropriate methods to evaluate performance based on several indicators. In practice, it is not possible to determine these indicators with exact values. In this paper, we proposed a model for determining the performance of two-stager systems in a fuzzy environment, also as well as the allocation of resources to it. We apply inverse data envelopment analysis and propose a multi-objective programming model that, by increasing the output of the unit under evaluation, determines the rate of increase of the first stage inputs and intermediate indicators in a way that maintains its efficiency. Then we illustrate the proposed model to allocate resources to bank branches.ﻋﻤﻮﻣﺎ ﺳﺎزﻣﺎنﻫﺎ و ﻣﻮﺳﺴﺎت دارای ﺑﺨﺶﻫﺎی ﻣﺘﻌﺪد داﺧﻠﯽ ﺑﻮده ﮐﻪ ﻋﻤﻠﮑﺮد ﮐﻠﯽ ﺳﺎزﻣﺎن، ﻧﺘﯿﺠﻪای از ﻋﻤﻠﮑﺮد ﻫﺮﯾﮏ از اﯾﻦ ﺑﺨﺶﻫﺎ ﯾﺎ ﻣﺮاﺣﻞ اﺳﺖ. ﻫﺮ ﺑﺨﺶ، دارای ﻋﻮاﻣﻞ ورودی و ﺧﺮوﺟﯽ ﺧﺎص ﺧﻮد ﻫﻤ ﭽﻨﯿﻦ ﻋﻮاﻣﻞ ارﺗﺒﺎط دﻫﻨﺪه ﺑﯿﻦ ﻣﺮاﺣﻞ اﺳﺖ. ﺷﺎﺧﺺﻫﺎی ﺑﯿﻦ ﻣﺮاﺣﻞ را ﺷﺎﺧﺺﻫﺎی ﻣﯿﺎﻧﯽ ﻣﯽﻧﺎﻣﻨﺪ. در ﯾﮏ ﺳﺎﺧﺘﺎر دوﻣﺮﺣﻠﻪای، ﺷﺎﺧﺺﻫﺎی ﻣﯿﺎﻧﯽ ﺧﺮوﺟﯽﻫﺎی ﻣﺮﺣﻠﻪ اول ﺑﻮده ﮐﻪ ﺑﻪﻋﻨﻮان ورودی ﻣﺮﺣﻠﻪ دوم ﺑﻪﮐﺎر ﻣﯽروﻧﺪ. ارزﯾﺎﺑﯽ ﻋﻤﻠﮑﺮد ﯾﮏ ﺳﺎزﻣﺎن ﺑﺎﯾﺪ ﺑﺎ درﻧﻈﺮ ﮔﺮﻓﺘﻦ ﻋﻤﻠﮑﺮد ﻫﺮﯾﮏ از ﺑﺨﺶﻫﺎی آن ﺗﻌﯿﯿﻦ ﮔﺮدد. ﺗﺤﻠﯿﻞ ﭘﻮﺷﺸﯽ دادهﻫﺎ ﯾﮑﯽ از روﺷﻬﺎی ﻣﻨﺎﺳﺐ ﺑﺮای ارزﯾﺎﺑﯽ ﻋﻤﻠﮑﺮد ﺑﺮاﺳﺎس ﭼﻨﺪ ﺷﺎﺧﺺ اﺳﺖ. در ﻋﻤﻞ ﺗﻌﯿﯿﻦ اﯾﻦ ﺷﺎﺧﺺﻫﺎ ﺑﺎ ﻣﻘﺎدﯾﺮ ﻗﻄﻌﯽ اﻣﮑﺎنﭘﺬﯾﺮ ﻧﻤﯽﺑﺎﺷﺪ. اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ اراﺋﻪ ﻣﺪﻟﯽ ﺟﻬﺖ ﺗﻌﯿﯿﻦ ﻋﻤﻠﮑﺮد ﻣﻮﺳﺴﺎت دوﺑﺨﺸﯽ در ﻣﺤﯿﻂ ﻓﺎزی ﻫﻤﭽﻨﯿﻦ ﺗﺨﺼﯿﺺ ﻣﻨﺎﺑﻊ ﺑﻪ آن ﻣﯽﭘﺮدازد. ﺑﺎ اﺳﺘﻔﺎده از ﺗﺤﻠﯿﻞ ﭘﻮﺷﺸﯽ دادهﻫﺎی ﻣﻌﮑﻮس، ﯾﮏ ﻣﺪل ﺑﺮﻧﺎﻣﻪرﯾﺰی ﭼﻨﺪﻫﺪﻓﻪ ﭘﯿﺸﻨﻬﺎد ﺷﺪه اﺳﺖ ﮐﻪ ﺑﺎ اﻓﺰاﯾﺶ ﺧﺮوﺟﯽﻫﺎی واﺣﺪ ﺗﺤﺖ ارزﯾﺎﺑﯽ، ﻣﯿﺰان اﻓﺰاﯾﺶ ورودیﻫﺎی ﻣﺮﺣﻠﻪ اول و ﻣﯿﺎﻧﯽ را ﺑﻨﺤﻮی ﺗﻌﯿﯿﻦ ﮐﻨﺪ ﮐﻪ ﮐﺎراﯾﯽ آن ﺣﻔﻆ ﺷﻮد. ﺳﭙﺲ ﻣﺪل ﭘﯿﺸﻨﻬﺎدی ﺑﺮای ﺗﺨﺼﯿﺺ ﻣﻨﺎﺑﻊ ﺑﻪ ﺷﻌﺐ ﺑﺎﻧﮏ اﺳﺘﻔﺎده ﺷﺪهاﺳﺖ.https://jfsa.fuzzy.ir/article_146083_8fb70905797784bc76848c6c9e63bc5b.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Bootstrap approach in hypothesis testing of variance for fuzzy random variablesرویکرد بوت استرپ در آزمون فرضیه برای واریانس متغیر تصادفی فازی18720414608410.22034/jfsa.2022.310120.1093FAاعظمشهرکیداﻧﺸﮑﺪه رﯾﺎﺿﯽ آﻣﺎر و ﻋﻠﻮم ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانمحمد حسیندهقانداﻧﺸﮑﺪه رﯾﺎﺿﯽ آﻣﺎر و ﻋﻠﻮم ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانحامداحمدزادهداﻧﺸﮑﺪه رﯾﺎﺿﯽ آﻣﺎر و ﻋﻠﻮم ﮐﺎﻣﭙﯿﻮﺗﺮ، داﻧﺸﮕﺎه ﺳﯿﺴﺘﺎن و ﺑﻠﻮﭼﺴﺘﺎن، زاﻫﺪان، اﯾﺮانJournal Article20211012The purpose of this paper is to express and calculate the variance confidence interval with the Chachi method of data conversion and without converting the initial data with the bootstrap method. Because the data conversion depends on the amount of variance in the denominator of the conversion expression, it fluctuates and as a result, estimating the confidence intervals and testing the hypothesis is challenged. The results of both methods are compared and their advantages and disadvantages are examined. For this purpose, they first deal with concepts such as the fuzzy random variable and the distance between fuzzy data based on the concept of α-shak as well as α -cut. The hypothesis testing process is then performed for the variance of a sample fuzzy data. The bootstrap method was used to test the hypothesis and the mentioned confidence intervals were compared with the confidence intervals obtained by α -bresh method.ﻫﺪف اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﯿﺎن و ﻣﺤ ﺎﺳﺒﻪ ﻓﺎﺻﻠ ﻪ اﻃﻤﯿﻨﺎن وارﯾﺎﻧﺲ ﺑﺎ روش ﭼﺎﭼﯽ )ﺗﺒﺪﯾﻞ دادهﻫﺎ( و ﺑﺪون ﺗﺒﺪﯾﻞ )دادهﻫﺎی اوﻟﯿﻪ( ﺑﺎ روش ﺑﻮتاﺳﺘﺮپ اﺳﺖ. ﺑﻪ دﻟﯿﻞ اﯾﻨﮑﻪ ﺗﺒﺪﯾﻞ دادهﻫﺎ ﺑﻪ ﻣﻘﺪار وارﯾﺎﻧﺲ در ﻣﺨﺮج ﻋﺒﺎرت ﺗﺒﺪﯾﻞ ﺑﺴﺘﮕﯽ دارد، دﭼﺎر ﻧﻮﺳﺎﻧﺎت ﺷﺪه و در ﻧﺘﯿﺠﻪ ﺑﺮآورد ﻓﺎﺻﻠﻪﻫﺎی اﻃﻤﯿﻨﺎن و آزﻣﻮن ﻓﺮﺿﯿﻪ ﺑﻪ ﭼﺎﻟﺶ ﮐﺸﯿﺪه ﻣﯽﺷﻮد. ﻧﺘﺎﯾﺞ ﻫﺮدو روش را ﻣﻘﺎﯾﺴﻪ و ﻣﺰاﯾﺎ و ﻣﻌﺎﯾﺐ آﻧﻬﺎ ﺑﺮرﺳﯽ ﻣﯽﺷﻮﻧﺪ. ﺑﺮای اﯾﻦ ﻣﻨﻈﻮر، در اﺑﺘﺪا ﺑﺎ ﻣﻔﺎﻫﯿﻤﯽ ﻣﺜﻞ ﻣﺘﻐﯿﺮ ﺗﺼﺎدﻓﯽ ﻓ ﺎزی و ﻓﺎﺻﻠ ﻪ ﺑﯿﻦ دادهﻫﺎی ﻓﺎزی ﺑﺮ اﺳﺎس ﻣﻔﻬﻮم -αﺷﮏ و ﻧﯿﺰ -αﺑﺮش ﭘﺮداﺧﺘﻪ ﻣﯽﺷﻮﻧﺪ. ﺳﭙﺲ ﻓﺮآﯾﻨﺪ آزﻣﻮن ﻓﺮﺿﯿﻪ را ﺑﺮای وارﯾﺎﻧﺲ دادهﻫﺎی ﻓﺎزی ﯾﮏ ﻧﻤﻮﻧﻪای اﻧﺠﺎم ﻣﯽﺷﻮد. در اﻧﺠﺎم آزﻣﻮن ﻓﺮﺿﯿﻪ از روش ﺑﻮتاﺳﺘﺮپ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ و ﻓﺎﺻﻠﻪﻫﺎی اﻃﻤﯿﻨﺎن ﻣﺬﮐﻮر را ﺑﺎ ﻓﺎﺻﻠﻪﻫﺎی اﻃﻤﯿﻨﺎن ﺣﺎﺻﻞ از روش -αﺑﺮش ﻣﻘﺎﯾﺴﻪ ﺷﺪه اﺳﺖ.https://jfsa.fuzzy.ir/article_146084_0a45f919f21eb0d967bbf76b0a7ac54c.pdfانجمن سیستم های فازی ایرانسیستم های فازی و کاربردها2717-44094220220220Fuzzy Dominating Number Based On Fuzzy Bridge And Applicaionsعدد احاطه گری فازی براساس پل فازی و کاربردهای آن20522914608510.22034/jfsa.2022.306606.1092FAمحمدحمیدیﮔﺮوه رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﭘﯿﺎم ﻧﻮر، ﺗﻬﺮان، اﯾﺮان0000-0002-8686-6942محمد اسماعیلنیکفرﮔﺮوه رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﭘﯿﺎم ﻧﻮر، ﺗﻬﺮان، اﯾﺮانJournal Article20210925The purpose of this paper is to apply of concept of fuzzy bridge and to extend of notation of fuzzy domination number in fuzzy graph. <br />Fuzzy bridge introduces a new notation of fuzzy domination sets and fuzzy domination numbers such that is consedering for fuzzy graphs with cyclics and vertices are located on cyclics. To check the importance of fuzzy domination numbers based on fuzzy bridge, we compare it to other distinct fuzzy domination numbers and show that fuzzy domination numbers based on fuzzy bridge is optimal.<br />The main method, in this research is based on computations of fuzzy domination number of complete fuzzy graphs with distinct fuzzy vertices and <br />is worked on special and useful fuzzy graphs such as strong 2-wheel fuzzy graph and 2-complete fuzzy graph. The study on 2-wheel fuzzy graph and 2-complete fuzzy graph was given for the first time in this paper and are worked via the generalize of 2-part of wheel fuzzy graph and complete fuzzy graph which these special fuzzy graphs have many applications on complex networks. <br />In this study, computations of fuzzy domination number of special fuzzy graphs are done <br />based on fuzzy bridge with distinct vertices. <br />The paper includes implications for the development of fuzzy graph, and for modeling the uncertainty problems and applicaions in real world which we consider two <br />modes of real problems.ﻫﺪف از اﯾﻦ ﻣﻘﺎﻟﻪ، ﺑﻪ ﮐﺎرﮔﯿﺮی ﻣﻔﻬﻮم ﭘﻞ ﻓﺎزی و ﺗﻮﺳﯿﻊ ﻧﻤﺎد ﻋﺪد اﺣﺎﻃﻪ ﮔﺮی در ﮔ ﺮافﻫﺎی ﻓ ﺎزی اﺳ ﺖ. ﭘﻞ ﻓ ﺎزی ﺑ ﻪ ﻣﻌ ﺮﻓ ﯽ ﻧ ﻤ ﺎد ﺟﺪﯾ ﺪی از ﻣﺠﻤﻮﻋ ﻪﻫﺎی اﺣﺎﻃﻪ ﮔﺮی و اﻋﺪاد اﺣﺎﻃﻪ ﮔﺮی ﻣﯽﭘﺮدازد ﮐﻪ ﺑﺮای ﮔﺮافﻫﺎی ﻓﺎزی ﮐﻪ ﺷﺎﻣﻞ دور ﻫﺴﺘﻨﺪ و رﺋﻮس روی دور ﻗﺮار ﻣﯽﮔﯿﺮﻧﺪ، ﻗﺎﺑﻞ ﺑﺮرﺳﯽ اﺳﺖ. ﺑﺮای ﺑﺮرﺳﯽ اﻫﻤ ﯿﺖ ﻋ ﺪد اﺣ ﺎﻃ ﻪ ﮔ ﺮی ﺑ ﺮ ﻣ ﺒ ﻨ ﺎی ﭘﻞ ﻓ ﺎزی ﺑ ﻪ ﻣﻘ ﺎﯾﺴ ﻪ اﻋ ﺪاد اﺣ ﺎﻃ ﻪ ﮔ ﺮی ﻣﺨﺘﻠﻔﯽ ﭘﺮداﺧﺘﯿﻢ و ﻧﺸﺎن داده اﯾﻢ ﮐﻪ ﻋﺪد اﺣﺎﻃﻪ ﮔﺮی ﺑﺮ ﭘﺎﯾﻪ ﭘﻞ ﻓﺎزی در ﺣﺎﻟﺖ ﺑﻬﯿﻨﻪ ﻗﺮار دارد. روش اﺻﻠﯽ ﻣﺎ در اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﺮ ﭘﺎﯾﻪ ﻣﺤﺎﺳﺒﻪ ﻋﺪد اﺣﺎﻃﻪ ﮔﺮی روی ﮔ ﺮافﻫﺎی ﻓ ﺎزی ﺑﺎ رﺋﻮس ﻓ ﺎزی ﻣﺘﻤﺎﯾ ﺰ اﺳﺖ و روی ﮔ ﺮافﻫﺎی ﺧ ﺎص و ﭘﺮﮐﺎرﺑﺮدی ﻣﺎﻧﻨﺪ ﮔﺮاف ﻓﺎزی ٢-ﭼﺮخ ﻗﻮی و ﮔﺮاف ﻓﺎزی ٢-ﮐﺎﻣﻞ ﻣﺤﺎﺳﺒﺎت اﻧﺠﺎم ﻣﯽدﻫﯿﻢ. ﻣﻄﺎﻟﻌﻪ روی ﮔﺮاف ﻓﺎزی ٢-ﭼﺮخ و ﮔﺮاف ﻓﺎزی ٢-ﮐﺎﻣﻞ ﺑﺮای ﻧﺨﺴﺘﯿﻦ ﺑ ﺎر در اﯾﻦ ﻣﻘ ﺎﻟ ﻪ و ﺑ ﺮاﺳ ﺎس ﺗﻌﻤﯿ ﻢ ٢-ﺑﺨﺸﯽ ﮔﺮاف ﻓ ﺎزی ﭼﺮخ و ﮔ ﺮاف ﻓ ﺎزی ﮐ ﺎﻣ ﻞ اﻧ ﺠ ﺎم ﮔ ﺮﻓ ﺘ ﻪ اﺳ ﺖ ﮐ ﻪ اﯾ ﻦ ﮔ ﺮافﻫﺎی ﻓ ﺎزی ﺧ ﺎص دارای ﮐﺎرﺑﺮدﻫﺎی زﯾﺎدی در ﺷﺒﮑﻪﻫﺎی ﻣﺨﺘﻠﻂ ﻫﺴﺘﻨﺪ. در اﯾﻦ ﻣﻄﺎﻟﻌﻪ، ﻣﺤﺎﺳﺒﺎت ﻋﺪد اﺣﺎﻃﻪ ﮔﺮی اﯾﻦ ﮔﺮافﻫﺎی ﻓﺎزی ﺧﺎص ﺑﺮاﺳﺎس ﭘﻞ ﻓﺎزی ﺑﺎ رﺋﻮس ﻣﺘﻤﺎﯾﺰ ﻓﺎزی اﻧﺠﺎم ﮔﺮﻓﺘﻪ اﺳﺖ. اﯾﻦ ﻣﻘﺎﻟﻪ ﺷﺎﻣﻞ اﻫﺪاﻓﯽ ﺑﺮای ﺗﻮﺳﻌﻪ ﮔﺮاف ﻓﺎزی در ﻣﺪل ﺳ ﺎزی ﻣﺴﺎﺋﻞ ﻋﺪم ﻗﻄﻌﯿﺖ و ﮐﺎرﺑ ﺮدﻫﺎی آن در دﻧﯿﺎی واﻗﻌﯽ اﺳﺖ ﮐﻪ ﺑﻪ دو ﻧﻤﻮﻧﻪ ﮐﺎرﺑﺮد واﻗﻌﯽ آن ﻣﯽﭘﺮدازﯾﻢhttps://jfsa.fuzzy.ir/article_146085_11636bca6db7c0faddf8fd09cdda4c34.pdf