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

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 Creating advice-taking reinforcement learners
نوع مقاله مقاله ژورنال
نویسندگان Richard Maclin, Jude W. Shavlik
چکیده / توضیح Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions, expressed as instructions in a simple imperative programming language. Based on techniques from knowledge-based neural networks, we insert these programs directly into the agent's utility function. Subsequent reinforcement learning further integrates and refines the advice. We present empirical evidence that investigates several aspects of our approach and shows that, given good advice, a learner can achieve statistically significant gains in expected reward. A second experiment shows that advice improves the expected reward regardless of the stage of training at which it is given, while another study demonstrates that subsequent advice can result in further gains in reward. Finally, we present experimental results that indicate our method is more powerful than a naive technique for making use of advice.
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عنوان اصلی مقاله Learning controllers for industrial robots
نوع مقاله مقاله ژورنال
نویسندگان C. Baroglio, A. Giordana, M. Kaiser, M. Nuttin, R. Piola
چکیده / توضیح One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn't sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is essential for achieving high performance. This paper discusses how Machine Learning has been applied to the design of (non-)linear controllers. Several alternative function approximators, including Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBFNs), and Fuzzy Controllers are analyzed and compared, leading to the definition of two major families: Open Field Function Approximators and Locally Receptive Field Function Approximators. It is shown that RBFNs and Fuzzy Controllers bear strong similarities, and that both have a symbolic interpretation. This characteristic allows for applying both symbolic and statistic learning algorithms to synthesize the network layout from a set of examples and, possibly, some background knowledge. Three integrated learning algorithms, two of which are original, are described and evaluated on experimental test cases. The first test case is provided by a robot KUKA IR-361 engaged into the “peg-into-hole” task, whereas the second is represented by a classical prediction task on the Mackey-Glass time series. From the experimental comparison, it appears that both Fuzzy Controllers and RBFNs synthesised from examples are excellent approximators, and that, in practice, they can be even more accurate than MLPs.
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عنوان اصلی مقاله Inferential theory of learning as a conceptual basis for multistrategy learning
نوع مقاله مقاله ژورنال
نویسندگان Ryszard S. Michalski
چکیده / توضیح In view of a great proliferation of machine learning methods and paradigms, there is a need for a general conceptual framework that would explain their interrelationships and provide a basis for their integration into multistrategy learning systems. This article presents initial results on theInferential Theory of Learning that aims at developing such a framework, with the primary emphasis on explaining logical capabilities of learning systems, i.e., theircompetence. The theory views learning as a goal-oriented process of modifying the learner's knowledge by exploring the learner's experience. Such a process is described as a search through aknowledge space, conducted by applying knowledge transformation operators, calledknowledge transmutations. Transmutations can be performed using any type of inference—deduction, induction, or analogy. Several fundamental pairs of transmutations are presented in a novel and very general way. These include generalization and specialization, explanation and prediction, abstraction and concretion, and similization and dissimilization. Generalization and specialization transmutations change thereference set of a description (the set of entities being described). Explanations and predictions derive additional knowledge about the reference set (explanatory or predictive). Abstractions and concretions change the level of detail in describing a reference set. Similizations and dissimilizations hypothesize knowledge about a reference set based on its similarity or dissimilarity with another reference set. The theory provides a basis formultistrategy task-adaptive learning (MTL), which is outlined and illustrated by an example. MTL dynamically adapts strategies to thelearning task, defined by the input information, the learner's background knowledge, and the learning goal. It aims at synergistically integrating a wide range of inferential learning strategies, such as empirical and constructive inductive generalization, deductive generalization, abductive derivation, abstraction, similization, and others.
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عنوان اصلی مقاله Balanced cooperative modeling
نوع مقاله مقاله ژورنال
نویسندگان Katharina Morik
چکیده / توضیح Machine learning techniques are often used for supporting a knowledge engineer in constructing a model of part of the world. Different learning algorithms contribute to different tasks within the modeling process. Integrating several learning algorithms into one system allows it to support several modeling tasks within the same framework. In this article, we focus on the distribution of work between several learning algorithms on the one hand and the user on the other hand. The approach followed by the MOBAL system is that ofbalanced cooperation, i.e., each modeling task can be done by the user or by a learning tool of the system. The MOBAL system is described in detail. We discuss the principle of multi-functionality of one representation for the balanced use by learning algorithms and users.
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عنوان اصلی مقاله Extracting refined rules from knowledge-based neural networks
نوع مقاله مقاله ژورنال
نویسندگان Geoffrey G. Towell, Jude W. Shavlik
چکیده / توضیح Neural networks, despite their empirically proven abilities, have been little used for the refinement of existing knowledge because this task requires a three-step process. First, knowledge must be inserted into a neural network. Second, the network must be refined. Third, the refined knowledge must be extracted from the network. We have previously described a method for the first step of this process. Standard neural learning techniques can accomplish the second step. In this article, we propose and empirically evaluate a method for the final, and possibly most difficult, step. Our method efficiently extracts symbolic rules from trained neural networks. The four major results of empirical tests of this method are that the extracted rules 1) closely reproduce the accuracy of the network from which they are extracted; 2) are superior to the rules produced by methods that directly refine symbolic rules; 3) are superior to those produced by previous techniques for extracting rules from trained neural networks; and 4) are “human comprehensible.” Thus, this method demonstrates that neural networks can be used to effectively refine symbolic knowledge. Moreover, the rule-extraction technique developed herein contributes to the understanding of how symbolic and connectionist approaches to artificial intelligence can be profitably integrated.
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عنوان اصلی مقاله Combining symbolic and neural learning
نوع مقاله مقاله ژورنال
نویسندگان Jude W. Shavlik
چکیده / توضیح Connectionist machine learning has proven to be a fruitful approach, and it makes sense to investigate systems that combine the strengths of the symbolic and connectionist approaches to AI. Over the past few years, researchers have successfully developed a number of such systems. This article summarizes one view of this endeavor, a framework that encompasses the approaches of several different research groups. This framework (see Figure 1) views the combination of symbolic and neural learning as a three-stage process: (1) the insertion of symbolic information into a neural network, thereby (partially) determining the topology and initial weight settings of a network, (2) the refinement of this network using a numeric optimization method such as backpropagation, possibly under the guidance of symbolic knowledge, and (3) the extraction of symbolic rules that accurately represent the knowledge contained in a trained network. These three components form an appealing, complete picture—approximately-correct symbolic information in, more-accurate symbolic information out—however, these three stages can be independently studied. In conclusion, the research summarized in this paper demonstrates that combining symbolic and connectionist methods is a promising approach to machine learning.
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عنوان اصلی مقاله Learning two-tiered descriptions of flexible concepts: The POSEIDON system
نوع مقاله مقاله ژورنال
نویسندگان F. Bergadano, S. Matwin, R. S. Michalski, J. Zhang
چکیده / توضیح This paper describes a method for learningflexible concepts, by which are meant concepts that lack precise definition and are context-dependent. To describe such concepts, the method employs atwo-tiered representation, in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In the proposed method, the first tier, calledBase Concept Representation (BCR), is created in two phases. In phase 1, the AQ-15 rule learning program is applied to induce a complete and consistent concept description from supplied examples. In phase 2, this description is optimized according to a domain-dependent quality criterion. The second tier, called theinferential concept interpretation (ICI), consists of a procedure forflexible matching, and a set of inference rules. The proposed method has been implemented in the POSEIDON system, and experimentally tested on two real-world problems: learning the concept of an acceptable union contract, and learning voting patterns of Republicans and Democrats in the U.S. Congress. For comparison, a few other learning methods were also applied to the same problems. These methods included simple variants of exemplar-based learning, and an ID-3-type decision tree learning, implemented in the ASSISTANT program. In the experiments, POSEIDON generated concept descriptions that were both, more accurate and also substantially simpler than those produced by the other methods.
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عنوان اصلی مقاله Smoking does not alter the dose-requirements and the pharmacodynamics of rocuronium
نوع مقاله مقاله ژورنال
نویسندگان Friedrich K. Pühringer MD, Philipp Keller CM, Alexander Löckinger MD, Axel Kleinsasser MD, Annkatrin Scheller CM, Claus Raedler CM, Christian Keller MD
چکیده / توضیح Purpose: Controversial data about the effect of smoking on the dose-requirements and the pharmacodynamics of rocuronium have been reported recently. This study was conducted to evaluate the dose-requirements and the pharmacodynamics of rocuronium in smokers using target controlled infusion.
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عنوان اصلی مقاله Toxizitätsdaten aus einer ökotoxikologischen Testbatterie
نوع مقاله مقاله ژورنال
نویسندگان Helga Neumann-Hensel, Stefan Pudenz
چکیده / توضیح Das Verhalten von Industriechemikalien in der Umwelt wird durch das Kompartiment Boden stark beeinflusst. Vor dem Hintergrund der komplexen Verflechtung der Bodeneigenschaften und der stoffspezifischen Parameter als Einflussgröße der Bioverfügbarkeit und des Abbauverhaltens ist es erforderlich, verschiedene Expostionssituationen mit Hilfe von ausgewählten ökotoxikologischen Tests abzubilden. Dazu wurde eine Testkombination eingesetzt, die sich bereits zur Untersuchung von Sedimenten als geeigner erwiesen hat.
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عنوان اصلی مقاله Nachhaltige Entwicklung von Managementstrategien: Multikriterielle Bewertungs- und Entscheidungshilfe-Instrumente im Umweltbereich
نوع مقاله مقاله ژورنال
نویسندگان Stefan Pudenz, Rainer Brüggemann, Kristina Voigt, Gerhard Welzi
چکیده / توضیح Zur Unterstützung von Bewertungs- und Entscheidungsprozessen über die Nachhaltigkeit von Managementstrategien, deren Auswirkungen durch unterschiedlich dimensionierte Indikatoren gemessen werden, gibt es verschiedene mathematische Methoden. In diesem Beitrag werden die Prinzipien dieser sog. multikriteriellen Bewertungs- und Entscheidungshilfeinstrumente beispielhaft anhand einer Auswahl von Strategien für ein Nachhaltiges Wassermanagement vorgestellt, sowie Vor- und Nachteile herausgearbeitet. Es wird gezeigt, dass sich die Verfahren insbesondere in Transparenz, Objektivität und durch den Grad an Partizipation durch Akteure z.T. erheblich unterscheiden. Während die Hassediagrammtechnik sich an den naturwissenschaftlich begründbaren Datenmatrix orientiert und somit eine objektive und transparente Bewertung und Datenanalyse liefert, haben Konkordanzanalyse, Nutzwertanalyse, PROMETHEE und AHP ihre Stärken in der Möglichkeit, Akteure bzw. Stakeholder am Entscheidungsprozess partizipieren zu lassen. *** DIRECT SUPPORT *** A00OI029 00003
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