This work utilizes the pattern recognition (PR) technologies by Client-Server network structure into a web-based recognizing system. The system uses a recurrent neural network (RNN) with associative memory to perform ...
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This paper proposes a novel real-time adaptive admission control (AAC) scheme with a desired quality of service (QoS) guarantee and high network utilization in high speed networks. The QoS is given in terms of service...
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This paper proposes a novel real-time adaptive admission control (AAC) scheme with a desired quality of service (QoS) guarantee and high network utilization in high speed networks. The QoS is given in terms of service delay, which is defined as the time it takes for a source to get admitted into the network after it initiates its intended request, packet/cell losses, and transmission delay (time taken to complete transmission from its initiation). AAC uses the following information - the available capacity from a novel adaptive bandwidth estimation scheme, a congestion indicator derived from a congestion controller, peak cell rate estimate from new sources, along with the desired QoS metrics, and outputs an 'admit' or 'reject' decision signal to the new sources while guaranteeing QoS and network utilization. Simulation results are presented by streaming ON/OFF and video data into the network. Results show that the proposed AAC admits significantly more traffic compared to other available admission control schemes thereby guaranteeing high network utilization while maintaining the desired QoS.
Computational intelligence techniques have been successfully used for solving control problems in p ache t-switching network architectures. The introduction of active networking adds a high degree of flexibility in cu...
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Computational intelligence techniques have been successfully used for solving control problems in packet-switching network architectures. The introduction of active networking adds a high degree of flexibility in cust...
Computational intelligence techniques have been successfully used for solving control problems in packet-switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introducing new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment.
An optimal learning scheme is proposed for a class of bidirectional associative memories (BAMs). This scheme, based on the perceptron learning algorithm, is motivated by the inadequacies/incompleteness of the weighted...
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An optimal learning scheme is proposed for a class of bidirectional associative memories (BAMs). This scheme, based on the perceptron learning algorithm, is motivated by the inadequacies/incompleteness of the weighted learning by global optimization, as derived by Wang et al. (1993). It is shown that the new scheme has superior properties: (1) Convergence to the correct solution, when it exists; and (2) A larger basin of attraction for the given set of patterns.
The authors present a novel adaptive control method for space-based robots which achieves globally stable trajectory tracking in the presence of uncertainties in the inertial parameters of the system. They begin with ...
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The authors present a novel adaptive control method for space-based robots which achieves globally stable trajectory tracking in the presence of uncertainties in the inertial parameters of the system. They begin with a partitioning of the fifteen-degree-of-freedom system dynamics into two component: a nine-degree-of-freedom invertible portion and a six-degree-of-freedom noninvertible portion. The controller is then designed to achieve trajectory tracking of the invertible portion of the system. This portion of the system consists of the manipulator joint positions and the orientation of the base. The motion of the noninvertible portion is bounded but unpredictable. This portion of the system consists of the position of the robot's base and the position of the reaction wheels. Simulation results are presented on a space-based robot with five revolute joints and one prismatic joint.< >
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