Pneumatic muscle (PM) has strong time varying characteristic. The complex nonlinear dynamics of PM system poses problems in achieving accurate modeling and control. To solve these challenges, we propose an echo state ...
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Spiking neural P systems with weights are a new variant of spiking neural P systems, where the applicability of rules is checked by a given potential threshold instead of the regular expression. There is a considerabl...
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Spiking neural P systems with weights are a new variant of spiking neural P systems, where the applicability of rules is checked by a given potential threshold instead of the regular expression. There is a considerable computational power hidden into the implicit mechanism that spiking neural P systems use to decide whether a given rule can be applied or not. In this work, several applications of spiking neural P systems with weights regarding their capability to solve some classical computer science problems are investigated. Specifically, three spiking neural P systems with weights are constructed, which can provide basic models for simulating a well-known parallel computational device-Boolean circuits. A family of spiking neural P systems with weights is also presented, in which a system of size k can perform the sorting of arbitrary k natural numbers in linear time with respect to the maximally natural number to be sorted. These applications of spiking neural P systems with weights partially show the computational power hidden into the mechanism of using a given potential threshold to check the applicability of rules
This paper is concerned with the problem of event-based H-infinity filtering for networked systems with communication delay (or signal transmission delay). We first propose a novel event-triggering scheme upon which t...
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This paper is concerned with the problem of event-based H-infinity filtering for networked systems with communication delay (or signal transmission delay). We first propose a novel event-triggering scheme upon which the sensor data is transmitted only when the specified event condition involving the sampled measurements of the plant is violated. By using delay system approach, a new model of filtering error system with state delay is formulated where the communication delay and event-triggering scheme are dealt with in a unified framework for networked systems. Then, by utilizing the Lyapunov-Krasovskii functional method plus free weighting matrix technique, sufficient conditions for ensuring the exponential stability as well as prescribed H-infinity performance for the filtering error system are derived in the form of linear matrix inequalities (LMIs). Based on these conditions, the explicit expression is given for the desired filter parameters. Finally, an illustrative example is presented to show the advantage of introducing the event-triggering scheme and the effectiveness of the proposed theoretical results. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of co...
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Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of connected neurons. However, in a biological system, there can be several synapses for each pair of connected neurons. In this study, inspired by this biological observation, synapses in a spiking neural P system are endowed with integer weight denoting the number of synapses for each pair of connected neurons. With the price of weight on synapses, quite small universal spiking neural P systems can be constructed. Specifically, a universal spiking neural P system with standard rules and weight having 38 neurons is produced as device of computing functions;as generator of sets of numbers, we find a universal system with standard rules and weight having 36 neurons.
It is difficult to guide the entry vehicle to prescribed area due to the disperse of environment and kinematics. Through predicted residual range at the current state based on drag acceleration, we developed a predict...
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Target detecting algorithm in infrared image is drawing extensive attention both at home and abroad, expecially when the infrared images own complex backgrounds and low resolution. How to make sure of the accuracy of ...
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This paper presents an active disturbance rejection guidance method using quadratic transition for the atmospheric ascent guidance problem. The quadratic transition is designed from the current flight states with a re...
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Pandemic influenza A (H1N1) has spread rapidly across the globe. In the event of pandemic influenza A (H1N1), decision-makers are required to act in the face of substantial uncertainties. Simulation models can be used...
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In this paper, we investigate the dynamics problem about the memristor-based recurrent network with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown ...
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In this paper, we investigate the dynamics problem about the memristor-based recurrent network with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that global exponential stability of such networks can be achieved when the external stimuli are sufficiently strong, without the need for other conditions. A sufficient condition on the bounds of stimuli is derived for global exponential stability of memristor-based recurrent networks. And all the results are in the sense of Filippov solutions. Simulation results illustrate the uses of the criteria to ascertain the global exponential stability of specific networks.
The paper introduces a general class of memristor-based recurrent neural networks with time-varying delays. Conditions on the nondivergence and global attractivity are established by using local inhibition, respective...
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The paper introduces a general class of memristor-based recurrent neural networks with time-varying delays. Conditions on the nondivergence and global attractivity are established by using local inhibition, respectively. Moreover, exponential convergence of the networks is studied by using local invariant sets. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. The obtained results extend some previous works on conventional recurrent neural networks. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.
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