The Experts below are selected from a list of 53034 Experts worldwide ranked by ideXlab platform
Raoul R Nigmatullin - One of the best experts on this subject based on the ideXlab platform.
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identification of the generalized weibull distribution in wind speed data by the eigen coordinates method
Renewable Energy, 2003Co-Authors: Mohammed Alhasan, Raoul R NigmatullinAbstract:The real distributions of local wind speed obtained from random temporal series have been analyzed in the framework of the new Eigen-coordinates method (ECs), which helps to identify a True Hypothesis with high level of authenticity. The season variations of the wind speed have been registered in Fjage (Jordan) and considered as initial data. It has been shown that histograms of the local wind speed were precisely described by the generalized Weibull model and the distribution model suggested by C. Tsallis should be rejected for present case. The possibilities and recommendations for the application of the ECs method to identify the theoretical curves needed for description of experimental data containing significant deviations are discussed. It has been shown that the ECs method could be used in these cases as a general approach and could be applied for analysis of temporal random series of different nature. PACS: 02.60.Ed., 02.60.Pn., 06.20.Dk., 07.05.Kf., 07.05.Rm.
Mohammed Alhasan - One of the best experts on this subject based on the ideXlab platform.
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identification of the generalized weibull distribution in wind speed data by the eigen coordinates method
Renewable Energy, 2003Co-Authors: Mohammed Alhasan, Raoul R NigmatullinAbstract:The real distributions of local wind speed obtained from random temporal series have been analyzed in the framework of the new Eigen-coordinates method (ECs), which helps to identify a True Hypothesis with high level of authenticity. The season variations of the wind speed have been registered in Fjage (Jordan) and considered as initial data. It has been shown that histograms of the local wind speed were precisely described by the generalized Weibull model and the distribution model suggested by C. Tsallis should be rejected for present case. The possibilities and recommendations for the application of the ECs method to identify the theoretical curves needed for description of experimental data containing significant deviations are discussed. It has been shown that the ECs method could be used in these cases as a general approach and could be applied for analysis of temporal random series of different nature. PACS: 02.60.Ed., 02.60.Pn., 06.20.Dk., 07.05.Kf., 07.05.Rm.
Sayed, Ali H. - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Social Learning
2021Co-Authors: Bordignon Virginia, Matta Vincenzo, Sayed, Ali H.Abstract:This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying Hypothesis (which means that the belief of every individual agent peaks at the True Hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. First, we provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong Hypothesis) at each individual agent. We carry out a large deviations analysis revealing the universal behavior of adaptive social learning: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate. Second, we characterize the adaptation performance by means of a detailed transient analysis, which allows us to obtain useful analytical formulas relating the adaptation time to the step-size
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Social learning under inferential attacks
2021Co-Authors: Ntemos Konstantinos, Bordignon Virginia, Vlaski Stefan, Sayed, Ali H.Abstract:A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong Hypothesis. The adversaries are unaware of the True Hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model.Comment: 5 pages, 2 figure
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Adaptive Social Learning
2020Co-Authors: Bordignon Virginia, Matta Vincenzo, Sayed, Ali H.Abstract:This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying Hypothesis (which means that the belief of every individual agent peaks at the True Hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. We provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong Hypothesis) at each individual agent. We also carry out a large deviations analysis revealing the universal behavior of adaptive social learner: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate
Bordignon Virginia - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Social Learning
2021Co-Authors: Bordignon Virginia, Matta Vincenzo, Sayed, Ali H.Abstract:This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying Hypothesis (which means that the belief of every individual agent peaks at the True Hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. First, we provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong Hypothesis) at each individual agent. We carry out a large deviations analysis revealing the universal behavior of adaptive social learning: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate. Second, we characterize the adaptation performance by means of a detailed transient analysis, which allows us to obtain useful analytical formulas relating the adaptation time to the step-size
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Social learning under inferential attacks
2021Co-Authors: Ntemos Konstantinos, Bordignon Virginia, Vlaski Stefan, Sayed, Ali H.Abstract:A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong Hypothesis. The adversaries are unaware of the True Hypothesis. However, they will "blend in" by behaving similarly to the other agents and will manipulate the likelihood functions used in the belief update process to launch inferential attacks. We will characterize the conditions under which the network is misled. Then, we will explain that it is possible for such attacks to succeed by showing that strategies exist that can be adopted by the malicious agents for this purpose. We examine both situations in which the agents have minimal or no information about the network model.Comment: 5 pages, 2 figure
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Adaptive Social Learning
2020Co-Authors: Bordignon Virginia, Matta Vincenzo, Sayed, Ali H.Abstract:This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their beliefs through local cooperation with their neighbors. Traditional social learning implementations are known to learn well the underlying Hypothesis (which means that the belief of every individual agent peaks at the True Hypothesis), achieving steady improvement in the learning accuracy under stationary conditions. However, these algorithms do not perform well under nonstationary conditions commonly encountered in online learning, exhibiting a significant inertia to track drifts in the streaming data. In order to address this gap, we propose an Adaptive Social Learning (ASL) strategy, which relies on a small step-size parameter to tune the adaptation degree. We provide a detailed characterization of the learning performance by means of a steady-state analysis. Focusing on the small step-size regime, we establish that the ASL strategy achieves consistent learning under standard global identifiability assumptions. We derive reliable Gaussian approximations for the probability of error (i.e., of choosing a wrong Hypothesis) at each individual agent. We also carry out a large deviations analysis revealing the universal behavior of adaptive social learner: the error probabilities decrease exponentially fast with the inverse of the step-size, and we characterize the resulting exponential learning rate
Nigmatullin R. - One of the best experts on this subject based on the ideXlab platform.
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Identification of the generalized Weibull distribution in wind speed data by the Eigen-coordinates method
2020Co-Authors: Al-hasan M., Nigmatullin R.Abstract:The real distributions of local wind speed obtained from random temporal series have been analyzed in the framework of the new Eigen-coordinates method (ECs), which helps to identify a True Hypothesis with high level of authenticity. The season variations of the wind speed have been registered in Fjage (Jordan) and considered as initial data. It has been shown that histograms of the local wind speed were precisely described by the generalized Weibull model and the distribution model suggested by C. Tsallis should be rejected for present case. The possibilities and recommendations for the application of the ECs method to identify the theoretical curves needed for description of experimental data containing significant deviations are discussed. It has been shown that the ECs method could be used in these cases as a general approach and could be applied for analysis of temporal random series of different nature. PACS: 02.60.Ed., 02.60.Pn., 06.20.Dk., 07.05.Kf., 07.05.Rm. © 2002 Elsevier Science Ltd. All rights reserved
