دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش هجدهم
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 | Optimization of information retrieval for cross media contents in a best practice network |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Pierfrancesco Bellini, Daniele Cenni, Paolo Nesi |
| چکیده / توضیح | Recent challenges in information retrieval are related to cross media information in social networks including rich media and web based content. In those cases, the cross media content includes classical file and their metadata plus web pages, events, blog, discussion forums, comments in multilingual. This heterogeneity creates large complex problems in cross media indexing and retrieval for services that integrate qualified documents and user generated content together. Problems are also related to scalability, robustness and resilience to errors. Moreover, users expect to have fast and efficient indexing and searching services, from social media in best practice network services. This paper presents a model and an indexing and searching solution for cross media contents, addressing the above issues, developed for the ECLAP Social Network, in the domain of Performing Arts. Effectiveness and optimization analysis of the retrieval solution are presented with relevant metrics. The research aimed to cope with the complexity of a heterogeneous indexing semantic model, using stochastic optimization techniques, with tuning and discrimination of relevant metadata terms. The research was conducted in the context of the ECLAP European Commission project and services (http://www.eclap.eu). |
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| عنوان اصلی مقاله | The Complexity of Lattice-Based Fuzzy Description Logics |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Stefan Borgwardt, Rafael Peñaloza |
| چکیده / توضیح | We study the complexity of reasoning in fuzzy description logics with semantics based on finite residuated lattices. For the logic SHI , we show that deciding satisfiability and subsumption of concepts, with or without a TBox, are ExpTime-complete problems. In ALCHI and a variant of SI , these decision problems become PSpace-complete when restricted to acyclic TBoxes. This matches the known complexity bounds for reasoning in crisp description logics between ALC and SHI . |
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| عنوان اصلی مقاله | Evolutionary intelligence in asphalt pavement modeling and quality-of-information |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | José Neves, Jorge Ribeiro, Paulo Pereira, Victor Alves, José Machado, António Abelha, Paulo Novais, Cesar Analide, Manuel Santos, Manuel Fernández-Delgado |
| چکیده / توضیح | The analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict the failure according to technical and non-technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric–mechanistic levels, and does not provide any suitable answer even for a single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have first-class environments at our disposal where defective information is at hand. To fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular in scenarios with incomplete information; it is an intelligence tool that advances according to the quality-of-information of the extensions of the predicates that model the universe of discourse. On the other hand, it is also considered the degree-of-confidence factor, a parameter that measures one‘s confidence on the list of characteristics presented by an asphalt pavement, set in terms of the attributes or variables that make the argument of the predicates referred to above. |
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| عنوان اصلی مقاله | Learning from streaming data with concept drift and imbalance: an overview |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | T. Ryan Hoens, Robi Polikar, Nitesh V. Chawla |
| چکیده / توضیح | The primary focus of machine learning has traditionally been on learning from data assumed to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such restrictions on the problem domain paved the way for development of elegant algorithms with theoretically provable performance guarantees. As is often the case, however, real-world problems rarely fit neatly into such restricted models. For instance class distributions are often skewed, resulting in the “class imbalance” problem. Data drawn from non-stationary distributions is also common in real-world applications, resulting in the “concept drift” or “non-stationary learning” problem which is often associated with streaming data scenarios. Recently, these problems have independently experienced increased research attention, however, the combined problem of addressing all of the above mentioned issues has enjoyed relatively little research. If the ultimate goal of intelligent machine learning algorithms is to be able to address a wide spectrum of real-world scenarios, then the need for a general framework for learning from, and adapting to, a non-stationary environment that may introduce imbalanced data can be hardly overstated. In this paper, we first present an overview of each of these challenging areas, followed by a comprehensive review of recent research for developing such a general framework. |
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| عنوان اصلی مقاله | Antifragility analysis and measurement framework for systems of systems |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | John Johnson, Adrian V. Gheorghe |
| چکیده / توضیح | The twenty-first century is defined by the social and technical hazards we face. A hazardous situation is a condition, or event, that threatens the well-being of people, organizations, societies, environments, and property. The most extreme of the hazards are considered X-Events and are an exogenous source of extreme stress to a system. X-Events can also be the unintended outputs of a system with both positive (serendipitous) and negative (catastrophic) consequences. Systems can vary in their ability to withstand these stress events. This ability exists on a continuum of fragility that ranges from fragile (degrading with stress), to robust (unchanged by stress), to antifragile (improving with stress). The state of the art does not include a method for analyzing or measuring fragility. Given that “what we measure we will improve,” the absence of a measurement approach limits the effectiveness of governance in making our systems less fragile and more robust if not antifragile. The authors present an antifragile system simulation model, and propose a framework for analyzing and measuring antifragility based on system of systems concepts. The framework reduces a multidimensional concept of fragility into a two-dimensional continuous interval scale. |
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| عنوان اصلی مقاله | Climate Change Impacts on Environmental Hazards on the Great Hungarian Plain, Carpathian Basin |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Gábor Mezősi, Teodóra Bata, Burghard C. Meyer, Viktória Blanka, Zsuzsanna Ladányi |
| چکیده / توضیح | The potential impacts of climate change on the Great Hungarian Plain based on two regional climate models, REMO and ALADIN, were analyzed using indicators for environmental hazards. As the climate parameters (temperature, precipitation, and wind) will change in the two investigated periods (2021–2050 and 2071–2100), their influences on drought, wind erosion, and inland excess water hazards are modeled by simple predictive models. Drought hazards on arable lands will increasingly affect the productivity of agriculture compared to the reference period (1961–1990). The models predict an increase between 12.3 % (REMO) and 20 % (ALADIN) in the first period, and between 35.6 % (REMO) and 45.2 % (ALADIN) in the second period. The increase of wind erosion hazards is not as obvious (+15 % for the first period in the REMO model). Inland excess water hazards are expected to be slightly reduced (−4 to 0 %) by both model predictions in the two periods without showing a clear tendency on reduction. All three indicators together give a first regional picture of potential hazards of climate change. The predictive model and data combinations of the regional climate change models and the hazard assessment models provide insights into regional and subregional impacts of climate change and will be useful in planning and land management activities. |
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| عنوان اصلی مقاله | Software testing optimization through test suite reduction using fuzzy clustering |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Gaurav Kumar, Pradeep Kumar Bhatia |
| چکیده / توضیح | Software testing is a most important but expensive activity. To get the most efficient and effective testing, test cases are designed on the basis of conditions. While designing test cases, many test cases are developed that are of no use or produced in duplicate. Exhaustive testing requires program execution with all possible combinations of values for program variables, which is impractical due to resource limitations. Redundant test cases or the test cases that are of no use, simply increases the testing effort and hence increases the cost. Our goal is to reduce the time spent in testing by reducing the number of test cases. For this we have incorporated fuzzy techniques to reduce the number of test cases so that more efficient and accurate results may be achieved. Fuzzy clustering is a class of algorithms for cluster analysis in which the allocation of similar test cases is done to clusters that would help in finding out redundancy incorporated by test cases. We proposed a methodology based on fuzzy clustering by which we can significantly reduce the test suite. The final test suite resulted from methodology will yield good results for conditions/path coverage. |
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| عنوان اصلی مقاله | Fuzzy-class point approach for software effort estimation using various adaptive regression methods |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Shashank Mouli Satapathy, Mukesh Kumar, Santanu Kumar Rath |
| چکیده / توضیح | The effort involved in developing a software product plays an important role in determining the success or failure. In the context of developing software using object oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. Software project managers require a reliable approach for effort estimation. It is especially important during the early stage of the software development life cycle. In this paper, the main goal is to estimate the cost of various software projects using class point approach and optimize the parameters using six types of adaptive regression techniques such as multi-layer perceptron, multivariate adaptive regression splines, projection pursuit regression, constrained topological mapping, K nearest neighbor regression and radial basis function network to achieve better accuracy. Also a comparative analysis of software effort estimation using these various adaptive regression techniques has been provided. By estimating the effort required to develop software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules. |
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| عنوان اصلی مقاله | Impulsive stabilization of fuzzy neural networks with time-varying delays |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | Xuyang Lou, Qian Ye, Baotong Cui |
| چکیده / توضیح | This paper is concerned with stabilization for a class of Takagi-Sugeno fuzzy neural networks (TSFNNs) with time-varying delays. An impulsive control scheme is employed to stabilize a TSFNN. We firstly establish the model of TSFNNs by using fuzzy sets and fuzzy reasoning and propose the problem of impulsive stabilization for this model. Then, we present several stabilization conditions based on Lyapunov function, inequality techniques and linear matrix inequality approach. Two numerical examples are provided to illustrate the efficiency of impulsive stabilization for TSFNNs by using fixed impulsive interval and variable impulsive intervals, respectively. |
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| عنوان اصلی مقاله | Fuzzy k∘ -preproximities related to fuzzy closure spaces |
| نوع مقاله | مقاله ژورنال |
| نویسندگان | A. H. Zakari, A. Ghareeb, Y. C. Kim |
| چکیده / توضیح | The aim of this paper is to define the concept of fuzzy k 0-preproximity and show how a fuzzy closure space is induced by a fuzzy k 0-preproximity and vice versa. Also, we introduce the notion of fuzzy k 0-preproximal neighborhood system. |
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