The Experts below are selected from a list of 28248 Experts worldwide ranked by ideXlab platform
Yuan Chen - One of the best experts on this subject based on the ideXlab platform.
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a convergent Smoothing Algorithm for training max min fuzzy neural networks
Neurocomputing, 2017Co-Authors: Zhijun Qiao, Yan Liu, Yuan ChenAbstract:Abstract In this paper, a smooth function is constructed to approximate the nonsmooth output of max –min fuzzy neural networks (FNNs) and its approximation is also presented. In place of the output of max –min FNNs by its Smoothing approximation function, the error function, defining the discrepancy between the actual outputs and desired outputs of max –min FNNs, becomes a continuously differentiable function. Then, a Smoothing gradient decent-based Algorithm with Armijo–Goldstein step size rule is formulated to train max –min FNNs. Based on the existing convergent result, the convergence of our proposed Algorithm can easily be obtained. Furthermore, the proposed Algorithm also provides a feasible procedure to solve fuzzy relational equations with max –min composition. Finally, some numerical examples are implemented to support our results and demonstrate that the proposed Smoothing Algorithm has better learning performance than other two gradient decent-based Algorithms.
Gu Jixing - One of the best experts on this subject based on the ideXlab platform.
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A New Rate Smoothing Algorithm Based on IP Streaming
Computer Engineering, 2004Co-Authors: Gu JixingAbstract:This paper proposes a streaming solution suited for delay-insensitive application such as video-on-demand. It addresses the implementation and the working sequence of both the server and the client. The rate Smoothing Algorithm based on client, which is called prediction- piecewise-Smoothing-Algorithm (PPSA), is stated in detail. It shields the jitter and ensures that the streaming output from the server is flat and stable, which lessens impact to the network.
Zhijun Qiao - One of the best experts on this subject based on the ideXlab platform.
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a Smoothing Algorithm with constant learning rate for training two kinds of fuzzy neural networks and its convergence
Neural Processing Letters, 2020Co-Authors: Zhijun Qiao, Zuqiang LongAbstract:In this paper, a Smoothing Algorithm with constant learning rate is presented for training two kinds of fuzzy neural networks (FNNs): max-product and max-min FNNs. Some weak and strong convergence results for the Algorithm are provided with the error function monotonically decreasing, its gradient going to zero, and weight sequence tending to a fixed value during the iteration. Furthermore, conditions for the constant learning rate are specified to guarantee the convergence. Finally, three numerical examples are given to illustrate the feasibility and efficiency of the Algorithm and to support the theoretical findings.
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a convergent Smoothing Algorithm for training max min fuzzy neural networks
Neurocomputing, 2017Co-Authors: Zhijun Qiao, Yan Liu, Yuan ChenAbstract:Abstract In this paper, a smooth function is constructed to approximate the nonsmooth output of max –min fuzzy neural networks (FNNs) and its approximation is also presented. In place of the output of max –min FNNs by its Smoothing approximation function, the error function, defining the discrepancy between the actual outputs and desired outputs of max –min FNNs, becomes a continuously differentiable function. Then, a Smoothing gradient decent-based Algorithm with Armijo–Goldstein step size rule is formulated to train max –min FNNs. Based on the existing convergent result, the convergence of our proposed Algorithm can easily be obtained. Furthermore, the proposed Algorithm also provides a feasible procedure to solve fuzzy relational equations with max –min composition. Finally, some numerical examples are implemented to support our results and demonstrate that the proposed Smoothing Algorithm has better learning performance than other two gradient decent-based Algorithms.
Anastasios I Mourikis - One of the best experts on this subject based on the ideXlab platform.
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motion tracking with fixed lag Smoothing Algorithm and consistency analysis
International Conference on Robotics and Automation, 2011Co-Authors: Tuecuong Dongsi, Anastasios I MourikisAbstract:This paper presents a fixed-lag Smoothing Algorithm for tracking the motion of a mobile robot in real time. The Algorithm processes measurements from proprioceptive (e.g., odometry, inertial measurement unit) and exteroceptive (e.g., camera, laser scanner) sensors, in order to estimate the trajectory of the vehicle. Smoothing is carried out in the information-filtering framework, and utilizes iterative minimization, which renders the method well-suited for applications where the effects of the measurements' nonlinearity are significant. The Algorithm attains bounded computational complexity by marginalizing out older states. The key contribution of this work is a detailed analysis of the effects of the marginalization process on the consistency properties of the estimator. Based on this analysis, a linearization scheme that results in substantially improved accuracy, compared to the standard linearization approach, is proposed. Both simulation and real-world experimental results are presented, which demonstrate that the proposed method attains localization accuracy superior to that of competing approaches.
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ICRA - Motion tracking with fixed-lag Smoothing: Algorithm and consistency analysis
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Tue-cuong Dong-si, Anastasios I MourikisAbstract:This paper presents a fixed-lag Smoothing Algorithm for tracking the motion of a mobile robot in real time. The Algorithm processes measurements from proprioceptive (e.g., odometry, inertial measurement unit) and exteroceptive (e.g., camera, laser scanner) sensors, in order to estimate the trajectory of the vehicle. Smoothing is carried out in the information-filtering framework, and utilizes iterative minimization, which renders the method well-suited for applications where the effects of the measurements' nonlinearity are significant. The Algorithm attains bounded computational complexity by marginalizing out older states. The key contribution of this work is a detailed analysis of the effects of the marginalization process on the consistency properties of the estimator. Based on this analysis, a linearization scheme that results in substantially improved accuracy, compared to the standard linearization approach, is proposed. Both simulation and real-world experimental results are presented, which demonstrate that the proposed method attains localization accuracy superior to that of competing approaches.
T.-c. Chang - One of the best experts on this subject based on the ideXlab platform.
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Signal enhancement of the spatial Smoothing Algorithm
IEEE Transactions on Signal Processing, 1991Co-Authors: A. Moghaddamjoo, T.-c. ChangAbstract:A spatial Smoothing Algorithm to decorrelate highly correlated sources for direction-of-arrival (DOA) estimation in narrowband problems is developed. The rate of decorrelation of the coherent sources is very low and sensitive to the signal-to-noise ratio (SNR) for sources with close DOAs. A method to enhance the signal (to remove the effects of the sensor noise) and to make the spatial Smoothing robust with respect to SNR is proposed. This approach will significantly improve the resolution of the Algorithm. Statistical characteristics of the improved spatial Smoothing are compared with that of the standard spatial Smoothing. >
