دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش هفدهم

منطق فازی (Fuzzy Logic) اولین بار در پی تنظیم نظریه مجموعه‌های فازی به وسیله پروفسور لطفی زاده (۱۹۶۵ میلادی) در صحنه محاسبات نو ظاهر شد. در واقع منطق فازی از منطق ارزش‌های «صفر و یک» نرم‌افزارهای کلاسیک فراتر رفته و درگاهی جدید برای دنیای علوم نرم‌افزاری و رایانه‌ها می‌گشاید، زیرا فضای شناور و نامحدود بین اعداد صفر و یک را نیز در منطق و استدلال‌های خود به کار می‌گیرد. در ادامه مقالات علمی انتشارات بین المللی اشپرینگر (Springer) در زمینه منطق فازی (Fuzzy Logic) برای دانلود آمده است. می توانید برای دانلود هر یک از مقالات از سرور دانلود متلب سایت، بر روی لینک دانلود هر یک از آن ها، کلیک کنید.

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دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — فهرست اصلی

عنوان اصلی مقاله Modeling of stage–discharge relationship for Gharraf River, southern Iraq using backpropagation artificial neural networks, M5 decision trees, and Takagi–Sugeno inference system technique: a comparative study
نوع مقاله مقاله ژورنال
نویسندگان Alaa M. Al-Abadi
چکیده / توضیح The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi–Sugeno (TS) inference system for mimic stage–discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models’ results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi–Sugeno techniques have certain advantages for setting up stage–discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage–discharge relationship in the study area.
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عنوان اصلی مقاله Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy models
نوع مقاله مقاله ژورنال
نویسندگان Alaa M. Al-Abadi
چکیده / توضیح In recent years, delineation of groundwater productivity zones plays an increasingly important role in sustainable management of groundwater resource throughout the world. In this study, groundwater productivity index of northeastern Wasit Governorate was delineated using probabilistic frequency ratio (FR) and Shannon’s entropy models in framework of GIS. Eight factors believed to influence the groundwater occurrence in the study area were selected and used as the input data. These factors were elevation (m), slope angle (degree), geology, soil, aquifer transmissivity (m2/d), storativity (dimensionless), distance to river (m), and distance to faults (m). In the first step, borehole location inventory map consisting of 68 boreholes with relatively high yield (>8 l/sec) was prepared. 47 boreholes (70 %) were used as training data and the remaining 21 (30 %) were used for validation. The predictive capability of each model was determined using relative operating characteristic technique. The results of the analysis indicate that the FR model with a success rate of 87.4 % and prediction rate 86.9 % performed slightly better than Shannon’s entropy model with success rate of 84.4 % and prediction rate of 82.4 %. The resultant groundwater productivity index was classified into five classes using natural break classification scheme: very low, low, moderate, high, and very high. The high–very high classes for FR and Shannon’s entropy models occurred within 30 % (217 km2) and 31 % (220 km2), respectively indicating low productivity conditions of the aquifer system. From final results, both of the models were capable to prospect GWPI with very good results, but FR was better in terms of success and prediction rates. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans.
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عنوان اصلی مقاله Fuzzy modeling of volume reduction of oil due to dissolved gas runoff and pressure release
نوع مقاله مقاله ژورنال
نویسندگان Ghassem Zargar, Parisa Bagheripour, Mojtaba Asoodeh
چکیده / توضیح Oil formation volume factor (FVF) refers to the change in oil volume between reservoir and standard conditions at surface. It is a crucial oil property which is governed by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. This parameter plays a trivial role in petroleum reservoir and production calculations. Accurate determination of oil FVF is restricted by limitations on reliable sampling and high cost and time-consumption associated with laboratory experiments. Furthermore, available empirical correlations do not have satisfying generalization and accuracy owing to being calibrated on specific oil samples. Therefore, this study offers a Takagi–Sugeno (TS) fuzzy logic model for estimating oil FVF for the purpose of developing a precise model calibrated on regional Iranian oil using 367 training samples. TS fuzzy model utilizes subtractive clustering approach for determining number of rules and clusters. Evaluation of constructed fuzzy logic using 108 unseen test data points indicated achievement of fuzzy logic in prediction of oil FVF.
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عنوان اصلی مقاله Development of an intelligent model for wax deposition in oil pipeline
نوع مقاله مقاله ژورنال
نویسندگان Mohammad Javad Jalalnezhad, Vahid Kamali
چکیده / توضیح Crude oil transport is one important part of the oil industry. Wax deposition is a very complex phenomenon that in recent years is one of the major challenges in oil industry. Wax deposited on the inner surface of crude oil pipelines are capable to reduce or completely stop the oil flow and the oil industry imposing large costs. The main objective of this study was to present a novel approach for predication of wax deposition thickness in single-phase turbulent flow rate. Using experimental data set and Adaptive neural-fuzzy inference system (ANFIS) model was developed. From the results predicted by this model, it can be pointed out that the ANFIS model can be used as powerful tools for prediction of wax deposition thickness in single-phase turbulent flow rate with mean square error, absolute relative deviation error and average absolute deviation error which are 0.00077034, 0.015720 and 0.097961, respectively.
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عنوان اصلی مقاله Committee machine reaping of three well-known models: established between saturation pressure and gas chromatography data
نوع مقاله مقاله ژورنال
نویسندگان Parisa Bagheripour, Mojtaba Asoodeh
چکیده / توضیح Saturation pressure is critical parameter of reservoir fluids which significantly affects petroleum engineering calculations. Accurate measurement of saturation pressure from laboratory experiment is very time, cost, and labor intensive. Therefore, it is favorable in most cases to achieve this parameter from empirical correlations. Three well-known models for estimation of saturation pressure from gas chromatography data include Elsharkawy model (EM), Soave–Redlich–Kwong (SRK), and Peng–Robinson (PR) equations of states (EOSs). This model proposes a novel approach, called committee machine, to reap beneficial advantages aforementioned three models through the combination of them. Committee machine produces a sophisticated model which performs in cooperation of EM, SRK and PR EOSs. Results indicated that CM model enhanced the accuracy of final prediction and performed more satisfyingly compared with individual model acting alone.
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عنوان اصلی مقاله Connecting Diverse Knowledge Systems for Enhanced Ecosystem Governance: The Multiple Evidence Base Approach
نوع مقاله مقاله ژورنال
نویسندگان Maria Tengö, Eduardo S. Brondizio, Thomas Elmqvist, Pernilla Malmer, Marja Spierenburg
چکیده / توضیح Indigenous and local knowledge systems as well as practitioners’ knowledge can provide valid and useful knowledge to enhance our understanding of governance of biodiversity and ecosystems for human well-being. There is, therefore, a great need within emerging global assessment programs, such as the IPBES and other international efforts, to develop functioning mechanisms for legitimate, transparent, and constructive ways of creating synergies across knowledge systems. We present the multiple evidence base (MEB) as an approach that proposes parallels whereby indigenous, local and scientific knowledge systems are viewed to generate different manifestations of knowledge, which can generate new insights and innovations through complementarities. MEB emphasizes that evaluation of knowledge occurs primarily within rather than across knowledge systems. MEB on a particular issue creates an enriched picture of understanding, for triangulation and joint assessment of knowledge, and a starting point for further knowledge generation.
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عنوان اصلی مقاله Fuzzy Model for Selection of Underground Mine Development System in a Bauxite Deposit
نوع مقاله مقاله ژورنال
نویسندگان Sasa Jovanovic, Zoran Gligoric, Cedomir Beljic, Branko Gluscevic, Cedomir Cvijovic
چکیده / توضیح In this paper, a fuzzy programming model, incorporating fuzzy measures of costs and ore reserves, is developed to evaluate different design alternatives in the context of the selection of the underground mine development system. The bauxite deposit is usually mined using the sublevel mining method. This method extracts the ore via sublevels, which are developed in the ore body at regular vertical spacing. In such an environment, we consider the development system as a weighted network interconnecting all sublevels with surface breakout point using the minimum cost of development and haulages. Selection of the optimal development system is based on the application of Convex Index and composite rank. The uncertainties related to the future states of transportation costs are modeled with a special stochastic process, the Geometric Brownian Motion. The results indicate that this model can be applied for solving underground mine development problems.
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عنوان اصلی مقاله Image metadata reasoning for improved clinical decision support
نوع مقاله مقاله ژورنال
نویسندگان Sonja Zillner, Daniel Sonntag
چکیده / توضیح Today, clinicians rely more and more on medical images for screening, diagnosis, treatment planning, and follow-up examinations. While medical images provide a wealth of information for clinicians, content information cannot be automatically integrated into advanced medical applications such as those for the clinical decision support. The implementation of advanced medical applications requires means for the automated post-processing of medical image annotations. In this article we describe how we made use of reasoning technologies to post-process medical image annotations in the context of the automated staging process of lymphoma patients. First, we describe how automatic anatomy detectors and OWL reasoning processes can be used to preprocess medical images automatically and in a way that makes accurate input to further, more complex reasoning processes possible. Second, we enhance and integrate patients’ image metadata by formalized practical clinical knowledge sources. The resulting combined data serve as input to an automatic reasoning process in order to stage lymphoma patients automatically.
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عنوان اصلی مقاله Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding
نوع مقاله مقاله ژورنال
نویسندگان Mehmet Üstündağ, Muammer Gökbulut, Abdulkadir Şengür, Fikret Ata
چکیده / توضیح The electrocardiogram (ECG) is a biological signal that contains important information about the cardiac activities of heart. ECG signal plays a very important role in the diagnosis and analysis of heart diseases. ECG signal is corrupted by various types of noise such as electrode movement, strong electromagnetic effect and muscle noise. Noisy ECG signal has been extracted using signal processing. This paper presents a weak ECG signal denoising method based on fuzzy thresholding and wavelet packet analysis. Firstly, the weak ECG signal is decomposed into various levels by wavelet packet transform. Then, the threshold value is determined using the fuzzy s-function. The reconstruction of the ECG signal from the retained coefficients is achieved by using inverse wavelet packet transform. We carried out several experiments to show the effectiveness of the proposed method and compared the results with the traditional wavelet packet soft and hard thresholding methods for weak signal denoising. The results are satisfactory according to calculated the correlation coefficient.
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عنوان اصلی مقاله Prediction of flow length in injection molding for engineering plastics by fuzzy logic under different processing conditions
نوع مقاله مقاله ژورنال
نویسندگان Aydin Salimi, Mehmet Subaşı, Lezgi Buldu, Çetin Karataş
چکیده / توضیح Flow length determination is one of the most important tasks in injection mold design. In order to achieve the perfect filling of the mold, proper designs for the channel depth and other injection parameters (such as melt temperature, injection pressure and etc.) should be conducted. In this research, melt temperature and injection pressure were considered as input parameters to investigate the flow length, in the most commonly used plastics including acrylonitrile butadiene styrene (ABS), polycarbonate (PC), polyamide 6.6 (PA 6.6), polyoxymethylene (POM). This study was carried out, based on various channel depths, according to ASTM D 3123. A new method based on a fuzzy logic method was developed to predict the amount of flow length in relation to the input parameters such as pressure, channel depth, and temperature. When the present method is used, the problem of finding the optimum mold design can be solved faster compared to the traditional modeling programs. The largest estimated amounts of flow lengths by the fuzzy logic model were 1215, 596, 963 and 1040 mm for POM, PC, PA 6.6 and ABS, respectively. The maximum measured values were 1235, 579, 948 and 1050 mm for the same material. Experimental tests have been conducted to justify the accuracy of the developed method. It was confirmed by regression analysis that the amount of R 2 for the measured and estimated values was 0.920529. The results of this research show that the fuzzy logic system is a reliable method to predict the short shots in an injection process.
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