دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش بیست و دوم

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.

این نوشته حاوی بخشی از مجموعه کامل مقالات است. برای دریافت سایر بخش ها، به لینک زیر مراجعه نمایید:

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

عنوان اصلی مقاله Optimization of hybrid renewable energy power systems: A review
نوع مقاله مقاله ژورنال
نویسندگان Binayak Bhandari, Kyung-Tae Lee, Gil-Yong Lee, Young-Man Cho, Sung-Hoon Ahn
چکیده / توضیح The characteristics of power produced from photovoltaic (PV) and Wind systems are based on the weather condition. Both the system are very unreliable in itself without sufficient capacity storage devices like batteries or back-up system like conventional engine generators. The reliability of the system significantly increases when two systems are hybridized with the provision of storage device. Even in such case, sufficient battery bank capacity is required to provide power to the load in extended cloudy days and non-windy days. Therefore the optimal sizing of system component represents the important part of hybrid power system. This paper summarizes recent trends of energy usage from renewable sources. It discusses physical modeling of renewable energy systems, several methodologies and criteria for optimization of the Hybrid Renewable Energy System (HRES). HRES is getting popular in the present scenario of energy and environmental crises. In this paper, we present a comprehensive review on the current state of optimization techniques specifically suited for the small and isolated power system based on the published literatures. The recent trend in optimization in the field of hybrid renewable energy system shows that artificial intelligence may provide good optimization of system without extensive long term weather data.
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عنوان اصلی مقاله An Empirical Approach for Determining Longitudinal Dispersion Coefficients in Rivers
نوع مقاله مقاله ژورنال
نویسندگان Sinan Sahin
چکیده / توضیح Determination of the longitudinal dispersion coefficient (LDC) of a river is needed in studies regarding cleaning the water and protecting its quality when nuclear, chemical or biological contaminants are discharged into the river. This study presents the development of an empirical equation for predicting the longitudinal dispersion coefficient in natural streams. Factors affecting the uniformity of the flow directly affect the LDC. Therefore, the hydraulic radius, defined as the ratio of the wetted area to the wetted perimeter, was considered as an important factor in determining the LDC. The presented equation relates the dispersion coefficient to hydraulic and geometric parameters of the flow, and was derived using dimensional and least squares analysis. The comparison of the predictions using 128 field data sets measured in 41 rivers in the USA has indicated that the proposed equation is reliable in predicting longitudinal dispersion coefficients in natural streams.
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عنوان اصلی مقاله A New Nonlinear Multiregression Model Based on the Lower and Upper Integrals
نوع مقاله مقاله ژورنال
نویسندگان Jing Chu, Zhenyuan Wang, Yong Shi, Kwong-Sak Leung
چکیده / توضیح A new nonlinear multiregression model based on a pair of extreme nonlinear integrals, lower and upper integrals, is established in this paper. A complete data set of predictive attributes and the relevant objective attribute is required for estimating the regression coefficients. Due to the nonadditivity of the model, a genetic algorithm combined with the pseudo gradient search is adopted to search the optimized solution in the regression problem. Applying such a nonlinear multiregression model, an interval prediction for the value of the objective attribute can be made once a new observation of predictive attributes is available.
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عنوان اصلی مقاله Proposal of New Objective Measures for Mining Association Rules: Cannibalization and Unexpectedness
نوع مقاله مقاله ژورنال
نویسندگان Hidenobu Hashikami, Masato Koda
چکیده / توضیح In view of the problems that a few of the existing measures for association rules does not directly meet user’s requirements, and association mining algorithms produce huge number of trivial rules, this paper proposes two new objective measures for mining association rules to solve the problems. The first measure is the degree of cannibalization between itemsets, which is bounded up with marketing strategy, and the second is the objective measure that intends to discover unexpected rules in the database. Experimental studies with application to public dataset and comparison of running time using synthetic datasets demonstrate the validity and effectiveness of the proposed measures.
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عنوان اصلی مقاله An EOQ Model for Time Dependent Backlogging Over Idle Time: A Step Order Fuzzy Approach
نوع مقاله مقاله ژورنال
نویسندگان Prasenjit Das, Sujit Kumar De, Shib Sankar Sana
چکیده / توضیح The present article deals with a backorder Economic Order Quantity (EOQ) model for natural leisure/closing time system where the demand rate depends upon the total shortage period and the seasonal effect. A cost minimization problem is developed by trading off setup cost, inventory cost, cost for idle time and shortage cost. The intuitionistic step order fuzzy number for optimization has been developed, assuming all parameters as fuzzy numbers. Ranking is done by employing score function, accuracy value on the centre of gravity and Euclidean distance function over the objective function. Finally numerical examples are considered to justify the model.
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عنوان اصلی مقاله System of Differential Equation with Initial Value as Triangular Intuitionistic Fuzzy Number and its Application
نوع مقاله مقاله ژورنال
نویسندگان Sankar Prasad Mondal, Tapan Kumar Roy
چکیده / توضیح In this paper, we solve a system of differential equation of first order with initial value as triangular intuitionistic fuzzy number. Two different cases are discussed: (i) coefficient is positive crisp number, (ii) coefficient is negative crisp number. Examples are given. We apply these procedures in Arm Race Model. Also we valuation, ambiguities and rank of fuzzy solution and defuzzify the solution.
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عنوان اصلی مقاله Profit Maximization Solid Transportation Problem with Trapezoidal Interval Type-2 Fuzzy Numbers
نوع مقاله مقاله ژورنال
نویسندگان Bimal Sinha, Amrit Das, Uttam Kumar Bera
چکیده / توضیح This paper proposes a new concept on transportation problem in which, we maximize the profit and minimize the transportation time while transporting an amount of quantity from a source to the destination. Here, we design two transportation models, in both the models; we maximize the profit and minimize the time of transportation. Here model-I having the unit purchase cost, unit selling price, unit transportation cost and transportation time as trapezoidal interval type-2 fuzzy number, while in model-II all the parameters are trapezoidal interval type-2 fuzzy number. To reduce these model-I and model-II into crisp equivalent, we use the expected value of a trapezoidal interval type-2 fuzzy number. Then the crisp equivalent problems are solved by employing the Interactive fuzzy satisficing method and LINGO 13.0 software to get the optimal solution. A numerical example is provided to demonstrate the models.
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عنوان اصلی مقاله Liquid matrix deposition on conductive hydrophobic surfaces for tuning and quantitation in UV-MALDI mass spectrometry
نوع مقاله مقاله ژورنال
نویسندگان Magnus Palmblad, Rainer Cramer
چکیده / توضیح With its highly fluctuating ion production matrix-assisted laser desorption/ionization (MALDI) poses many practical challenges for its application in mass spectrometry. Instrument tuning and quantitative ion abundance measurements using ion signal alone depend on a stable ion beam. Liquid MALDI matrices have been shown to be a promising alternative to the commonly used solid matrices. Their application in areas where a stable ion current is essential has been discussed but only limited data have been provided to demonstrate their practical use and advantages in the formation of stable MALDI ion beams. In this article we present experimental data showing high MALDI ion beam stability over more than two orders of magnitude at high analytical sensitivity (low femtomole amount prepared) for quantitative peptide abundance measurements and instrument tuning in a MALDI Q-TOF mass spectrometer. Samples were deposited on an inexpensive conductive hydrophobic surface and shrunk to droplets <10 nL in size. By using a sample droplet <10 nL it was possible to acquire data from a single irradiated spot for roughly 10,000 shots with little variation in ion signal intensity at a laser repetition rate of 5–20 Hz.
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عنوان اصلی مقاله An efficient data format for mass spectrometry-based proteomics
نوع مقاله مقاله ژورنال
نویسندگان Anuj R. Shah, Jennifer Davidson, Matthew E. Monroe, Anoop M. Mayampurath, William F. Danielson, Yan Shi, Aaron C. Robinson, Brian H. Clowers, Mikhail E. Belov, Gordon A. Anderson, Richard D. Smith
چکیده / توضیح The diverse range of mass spectrometry (MS) instrumentation along with corresponding proprietary and nonproprietary data formats has generated a proteomics community driven call for a standardized format to facilitate management, processing, storing, visualization, and exchange of both experimental and processed data. To date, significant efforts have been extended towards standardizing XML-based formats for mass spectrometry data representation, despite the recognized inefficiencies associated with storing large numeric datasets in XML. The proteomics community has periodically entertained alternate strategies for data exchange, e.g., using a common application programming interface or a database-derived format. However, these efforts have yet to gain significant attention, mostly because they have not demonstrated significant performance benefits over existing standards, but also due to issues such as extensibility to multidimensional separation systems, robustness of operation, and incomplete or mismatched vocabulary. Here, we describe a format based on standard database principles that offers multiple benefits over existing formats in terms of storage size, ease of processing, data retrieval times, and extensibility to accommodate multidimensional separation systems.
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عنوان اصلی مقاله Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra
نوع مقاله مقاله ژورنال
نویسندگان Peter de B. Harrington, Kent J. Voorhees, Franco Basile, Alan D. Hendricker
چکیده / توضیح Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 ± 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.
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