دانلود رایگان مجموعه مقالات علمی اشپرینگر در زمینه منطق فازی — بخش سی و سوم

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 Intelligent feature selection and classification techniques for intrusion detection in networks: a survey
نوع مقاله مقاله ژورنال
نویسندگان Sannasi Ganapathy, Kanagasabai Kulothungan, Sannasy Muthurajkumar, Muthusamy Vijayalakshmi, Palanichamy Yogesh, Arputharaj Kannan
چکیده / توضیح Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.
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عنوان اصلی مقاله Fingerprint indoor positioning algorithm based on affinity propagation clustering
نوع مقاله مقاله ژورنال
نویسندگان Zengshan Tian, Xiaomou Tang, Mu Zhou, Zuohong Tan
چکیده / توضیح Recently, the fingerprint-based wireless local area network (WLAN) positioning has gained significant interest. A probability distribution-aided indoor positioning algorithm based on the affinity propagation clustering is proposed. Different from the conventional fingerprint-based WLAN positioning algorithms, the paper first utilizes the affinity propagation clustering to minimize the searching space of reference points (RPs). Then, we introduce the probability distribution-aided positioning algorithm to obtain the target's refined position. Furthermore, because the affinity clustering can effectively lead to a reduction of the computational cost for the RP searching which is involved in the probability distribution-aided positioning algorithm, the proposed algorithm can lower the difficulty and minimize the power consumption when estimating the user's position. Experimental results conducted in the real environments show that our proposed algorithm will significantly improve the performance of the probability distribution-aided positioning algorithm in both the positioning accuracy and real-time ability.
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عنوان اصلی مقاله Self-similarity property of acoustic data acquired in shallow water environment
نوع مقاله مقاله ژورنال
نویسندگان Jin Chen, Fa-jie Duan, Jia-jia Jiang, Baoju Zhang, Ying Tong, Jun He
چکیده / توضیح Underwater acoustic modeling in shallow water environment is difficult since sound waves reflect several times between the surface and the water bottom. This article discusses an underwater acoustic characteristics analysis method based on self-similarity. It is found that acoustic signal has good self-similarity in shallow water. The actual towed hydrophone linear array was established and it was used for underwater acoustic signal acquisition experiment in Qilihai Reservoir which is located in the suburb of Tianjin, China. It can be derived that the signals acquired by hydrophones have self-similarity by the analysis of the variance of m-aggregated time series. It is proved that the characteristics of self-similarity can be used for the sound pulse propagation in shallow water.
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عنوان اصلی مقاله Particle swarm optimization (PSO)-based node and link lifetime prediction algorithm for route recovery in MANET
نوع مقاله مقاله ژورنال
نویسندگان Devi Manickavelu, Rhymend Uthariaraj Vaidyanathan
چکیده / توضیح In the conventional mobile ad hoc network (MANET) systems' route rediscovery methods, there exists route failure in all route discovery methods resulting in data loss and communication overheads. Hence, the routing has to be done in accordance with mobility character of the network. In this manuscript, a particle swarm optimization (PSO)-based lifetime prediction algorithm for route recovery in MANET has been proposed. This technique predicts the lifetime of link and node in the available bandwidth based on the parameters like relative mobility of nodes and energy drain rate, etc. Using predictions, the parameters are fuzzified and fuzzy rules have been formed to decide on the node status. This information is made to exchange among all the nodes. Thus, the status of every node is verified before data transmission. Even for a weak node, the performance of a route recovery mechanism is made in such a way that corresponding routes are diverted to the strong nodes. With the aid of the simulated results, the minimization of data loss and communication overhead using PSO prediction has been discussed in detail.
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عنوان اصلی مقاله QoS-aware composite scheduling using fuzzy proactive and reactive controllers
نوع مقاله مقاله ژورنال
نویسندگان Nabeel Khan, Maria G Martini, Dirk Staehle
چکیده / توضیح We consider in this paper downlink scheduling for different traffic classes at the MAC layer of wireless systems based on orthogonal frequency division multiple access (OFDMA), such as the recent 3rd Generation Partnership Project (3GPP) long-term evolution (LTE)/LTE-A wireless standard. Our goal is to provide via the scheduling decisions quality of service (QoS), but also to guarantee fairness among the different users and traffic classes (including delay-sensitive and best-effort traffic). QoS-aware scheduling strategies, such as modified largest weighted delay first (M-LWDF), exponential (EXP), exponential proportional fair (EXP-PF), and the log-based scheduling rules, prioritize delay-sensitive traffic by considering rules based on the head-of-line (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffic by considering the classical proportional fair (PF) rule. These scheduling rules do not prevent resource starvation for best-effort traffic. On the other side, if both traffic types are scheduled according to the PF rule, then delay-sensitive flows suffer from delay bound violations. In order to fairly distribute the resources among different service classes according to their QoS requirements and channel conditions, we employ the concept of fuzzy logic in our scheduling framework. By employing the fuzzy logic concept, we serve all the traffic classes with one priority rule. Simulation results show better intra-class and inter-class fairness than state-of-the-art scheduling rules. The proposed scheduling framework enables to appropriately balance delay requirements of traffic, system throughput, and fairness.
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عنوان اصلی مقاله Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series
نوع مقاله مقاله ژورنال
نویسندگان Rajnish K Yadav, Manoj Balakrishnan
چکیده / توضیح Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference system (ANFIS) and autoregressive integrated moving average (ARIMA) for modeling of wireless network traffic in terms of typical statistical indicator and computational complexity has been attempted. ARIMA has been widely used in this area for past many years. On the other hand, ANFIS is comparatively new, and no network traffic modeling using ANFIS was attempted until recently to the best of our knowledge. At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature. Reportedly, ANFIS provides a good precision in prediction in terms of statistical indicators and also gives effective description of network conditions at different times. However, the computational complexity of ANFIS for traffic modeling is a major concern and deserves a closer inspection. In our case of wireless network traffic, as a final result, we find that ANFIS model performs better than the best ARIMA model in three different scenarios.
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عنوان اصلی مقاله Vehicle interconnection metric and clustering protocol for improved connectivity in vehicular ad hoc networks
نوع مقاله مقاله ژورنال
نویسندگان Samo Vodopivec, Melita Hajdinjak, Janez Bešter, Andrej Kos
چکیده / توضیح Communication is the main driving force behind the emerging intelligent transportation systems, which are expected to make traveling safer, more ecological, and faster. The most challenging among all the different communication technologies that will be used are the direct vehicle-to-vehicle communications, because vehicles move with high speeds and in different directions. Apart from that, it is expected that vehicles should be able to organize themselves in an ad hoc network without any assistance from outside entities, such as road side units. To make the ad hoc network less dynamic and communications more reliable, vehicles with similar movement patterns can be grouped together in clusters. Clustering is a well-known method for organizing ad hoc networks and is used in mobile and wireless sensor networks, but with different constraints and goals, so new clustering solutions for vehicular ad hoc networks (VANET) have to be developed. In recent years, this has been a hot topic among researchers and many different clustering algorithms for VANET have been proposed. In this paper, we propose a new clustering metric for VANET, named vehicle interconnection metric, which is based on sending periodic beacons among vehicles and reflects the communication abilities between them. We also propose a new clustering algorithm whose primary goal is increased connectivity and lower number of disconnects. The working principle of this algorithm is also inverted compared to others and uses unneeded cluster head elimination instead of cluster head election. Mathematical analysis of memory usage and communication overhead are provided, predicting low-resource usage. Simulation results, obtained with the ns-3 network simulator and the SUMO vehicle movement simulator, have confirmed the analysis and expected performance in terms of cluster head duration, number of connectivity losses and role switches.
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عنوان اصلی مقاله Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges
نوع مقاله مقاله ژورنال
نویسندگان Kamal Deep Singh, Priyanka Rawat, Jean-Marie Bonnin
چکیده / توضیح With growing interest in using cognitive radio (CR) technology in wireless communication systems for vehicles, it is envisioned that future vehicles will be CR-enabled. This paper discusses CR technologies for vehicular networks aimed at improving vehicular communication efficiency. CR for vehicular networks has the potential of becoming a killer CR application in the future due to a huge consumer market for vehicular communications. This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology in vehicular ad hoc networks. We review how CR technologies such as dynamic spectrum access, adaptive software-defined radios, and cooperative communications will enhance vehicular communications and, hence, present the potential of transforming vehicle communication in terms of efficiency and safety. Our work is different from existing works in that we provide recent advances and open research directions on applying cognitive radio in vehicular ad hoc networks (CR-VANETs) focusing on architecture, machine learning, cooperation, reprogrammability, and spectrum management as well as QoE optimization for infotainment applications. A taxonomy of recent advances in cognitive radio for vehicular networks is also provided. In addition, several challenges and requirements have been identified. The research on applying CR in vehicular networks is still in its early stage, and there are not many experimental platforms due to their complex setup and requirements. Some related testbeds and research projects are provided at the end.
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عنوان اصلی مقاله Fuzzy-based congestion control for wireless multimedia sensor networks
نوع مقاله مقاله ژورنال
نویسندگان Cagatay Sonmez, Ozlem Durmaz Incel, Sinan Isik, Mehmet Yunus Donmez, Cem Ersoy
چکیده / توضیح Congestion is a challenging problem for sensor networks because it causes the waste of communication and reduces energy efficiency. Compared to traditional wireless sensor networks, the probability of congestion occurrence in wireless multimedia sensor networks is higher due to the high volume of data arising from multimedia streaming. In this article, problems for multimedia transmission over wireless multimedia sensor networks are examined and sensor fuzzy-based image transmission (SUIT); a new progressive image transport protocol is proposed as a solution. SUIT provides fuzzy logic-based congestion estimation and an efficient congestion mitigation technique which decreases the image quality on-the-fly to an acceptable level. In case of congestion, SUIT drops some packets of the frames in a smart way and thus transmits frames to the sink with lower, but acceptable quality. In this way, SUIT improves the continuity of the video streaming. We evaluate the performance of SUIT by comparing it with two different competitors. The first one is an example transport protocol, namely Fuzzy Logic-Based Congestion Estimation. The second one is a buffer occupancy-based congestion control mechanism which is commonly used in previous studies. According to the simulation results, SUIT provides better energy consumption, frame delivery, frame loss and frame latency performance than its competitors.
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عنوان اصلی مقاله Real-time urban traffic amount prediction models for dynamic route guidance systems
نوع مقاله مقاله ژورنال
نویسندگان Zilu Liang, Yasushi Wakahara
چکیده / توضیح The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do.
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