Hypertension, one of the most common cardiovascular diseases, may not have obvious symptoms in its early stages, making it difficult to detect through simple blood pressure tests. A deep learning method using electroc...
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The clever pass-layer layout for congestion management in wireless networks is a complete approach that leverages the interaction between community layers to develop efficient, dependable, and cozy Wi-Fi networks. Thi...
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Battery management system analysis and design for mobile device battery monitoring is a revolutionary approach that incorporates the capability the battery which contains the battery management system into the mobile ...
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Quality of service (QoS) provisioning is critical to real-time applications such as VOIP and IPTV, but has not been resolved in multihop networks such as mesh and ad hoc networks. For multiple access in ubiquitous net...
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In order to achieve the real-time neutron flux monitoring in the presence of high-level mixed neutrons and background rays, a Field Programmable Gate Array (FPGA)-based automatic Gain control Digital Time-division Int...
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Software Defined Networks (SDN) provides separation of data plane and control plane, which can be used for implementing various network solutions like traffic engineering, intrusion detection load balancing, etc. Howe...
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Peer review represents the status-quo when it comes to evaluating research articles that are submitted to conferences and journals. The significance of a computer science article is given by the prestige of the public...
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A modulation recognition method based on multi-resolution analysis for time-frequency overlapped multi-signals is proposed. These signals include wireless communication signals, RADAR signals and satellites signals in...
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The adjustment of parameters within a function for modeling a set of observations is a very frequent task in many applied areas of science. There are sophisticated techniques to reach this goal, such as regression, us...
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The adjustment of parameters within a function for modeling a set of observations is a very frequent task in many applied areas of science. There are sophisticated techniques to reach this goal, such as regression, use of gradients, neural networks, neurofuzzy modeling, genetic algorithms, swarm optimization, etc. In this paper numerical simulations are done about the efficiency and capacity of the Least Mean Square (LMS) algorithm to find an optimal set of parameters for adjusting a function to a set of observed data. Although the LMS method has been very used for minimization of errors and extraction of noise in signal processing systems, its capacities for regression and approximation have been not very often explored. Using simple examples, conditions on which the learning parameters can be adjusted to model a set of training data are explored, using a iterative learning process where the approximation of the stochastic error is recalculated immediately after any parameter is actualized. A description of the speed for convergence, as a function of the learning rate, is shown for the cases under study.
As to the control of fuzzy sliding mode, this paper proposes a cerebellar learning model for on-line learning of the controller. Fuzzy sliding mode has excellent robustness to the system uncertainty and immunity to th...
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