A variety of problems in operations research, performance analysis, manufacturing, and communication networks, etc., can be modelled as discrete event systems with minimum and maximum constraints. When such systems re...
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A variety of problems in operations research, performance analysis, manufacturing, and communication networks, etc., can be modelled as discrete event systems with minimum and maximum constraints. When such systems require only maximum constraints (or dually, only minimum constraints), they can be studied using linear methods based on max-plus algebra. systems with mixed constraints are called min-max systems in which rain, max and addition operations appear simultaneously. A significant amount of work on such systems can be seen in literature. In this paper we provide some new results with regard to the balance problem of min-max functions; these are the structure properties of min-max systems. We use these results in the structural stabilization. Our main results are two sufficient conditions for the balance and one sufficient condition for the structural stabilization. The block technique is used to analyse the structure of the systems. The proposed methods, based on directed graph and max-plus algebra are constructive in nature. We provide several examples to demonstrate how the methods work in practice.
作者:
Chang-Woo ShinSeunghwan Kim[]Asia Pacific Center for Theoretical Physics
National Core Research Center for System Biodynamics and Nonlinear and Complex Systems Laboratory Department of Physics Pohang University of Science and Technology San 31 Hyoja-dong Nam-gu Pohang Gyungbuk Korea 790-784
Recent studies on complexsystems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We sho...
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Recent studies on complexsystems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks. We show that the functional structures in the brain can be self-organized to both small-world and scale-free networks by synaptic reorganization via spike timing dependent synaptic plasticity instead of conventional Hebbian learning rules. We show that the balance between the excitatory and the inhibitory synaptic inputs is critical in the formation of the functional structure, which is found to lie in a self-organized critical state.
Integral-controller particle swarm optimization (ICPSO), influenced by inertia weight w and coefficient ψ is a new swarm technology by adding accelerator information. Based on stability analysis, the convergence cond...
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ISBN:
(纸本)9781424404759
Integral-controller particle swarm optimization (ICPSO), influenced by inertia weight w and coefficient ψ is a new swarm technology by adding accelerator information. Based on stability analysis, the convergence conditions imply the negative selection principles of inertia weight w, and the relationship between w and ψ. To improve the computational efficiency, an adaptive strategy for tuning the parameters of ICPSO is described using a new statistical variable reflecting computational efficiency index-average accelerator information. The optimization computing of some examples is made to show that the ICPSO has better global search capacity and rapid convergence speed.
One of the issues of autonomous Web services is that precise business protocols across systems are not always predefined. A likely solution to this problem is to dynamically generate business protocols by matching ext...
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The standard particle swarm optimization (PSO) may prematurely converge on suboptimal solution partly because of the insufficiency information utilization of the velocity. The time cost by velocity is longer than posi...
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ISBN:
(纸本)1424404754;9781424404759
The standard particle swarm optimization (PSO) may prematurely converge on suboptimal solution partly because of the insufficiency information utilization of the velocity. The time cost by velocity is longer than position of each particle of the swarm, though the velocity, limited by the constant Vmax, onfy provides the positional displacement To avoid premature convergence, a new modified PSO, predicted PSO, is proposed owning two different swarms in which the velocity without limitation, considered as a predictor, is used to explore the search space besides providing the displacement while the position considered as a corrector. The algorithm gives some balance between global and local search capability. The optimization computing of some examples is made to show the new algorithm has better global search capacity and rapid convergence rate.
On the basis of the standard SVM for regression, the dynamic Ε-SVM method was proposed to establish precise mathematical models to describe the behavior of biochemistry systems, namely each training sample used diffe...
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On the basis of the standard SVM for regression, the dynamic Ε-SVM method was proposed to establish precise mathematical models to describe the behavior of biochemistry systems, namely each training sample used different error. At the same time, an improved multi-objective Genetic Algorithm (MOGA) was used to automatically select the dynamic Ε-SVM parameters. A new modeling method that combined improved MOGA with dynamic Ε-SVM regression was presented. The model for titer pre-estimate was developed in Matlab6.5 with data collected from real plant. The model possessed the strong capability of fitting and generalization. It is shown that the method achieves significant improvement in the generalization performance in comparison with the modeling method based on MOGA and the standard SVM.
作者:
Sang-Gui LeeSeunghwan KimAsiaPacific Center for Theoretical Physics
National Core Research Center on System BioDynamics and Nonlinear and Complex Systems Laboratory Department of Physics Pohang University of Science and Technology San 31 Hyojadong Pohang Korea 790-784
The phenomena of stochastic resonance (SR) has attracted much attention in the studies of the excitable systems, in particular, the nervous systems under noise. Recently, an alternative SR condition, called the bona f...
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The phenomena of stochastic resonance (SR) has attracted much attention in the studies of the excitable systems, in particular, the nervous systems under noise. Recently, an alternative SR condition, called the bona fide SR, was proposed for the bistable system under noise, based on the notion of the residence time distribution. As the forcing frequency increases, there exists an optimal resonant frequency. We study the SR in a stochastic Hodgkin-Huxley neuron, which has an inherent natural frequency in addition to the stochastic time scale. We have observed two resonant conditions; one between periodic forcing and natural frequencies, and the other between the periodic forcing and the stochastic frequencies. These resonance conditions show the bona fide stochastic resonance with multimodality. For comparison, we have studied the bona fide SR in the stochastic FitzHugh-Nagumo neuron, where, the multimodality is not observed. The differences in the resonance structure of two neuron models are understood in terms of differences in the phase portraits.
In this paper, the micromanipulation system for the MEMS assembly is constructed. An improved focus measure and clamping strategy are proposed for the assembly of the MEMS. The focus measure is used to focusing the mi...
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In this paper, the micromanipulation system for the MEMS assembly is constructed. An improved focus measure and clamping strategy are proposed for the assembly of the MEMS. The focus measure is used to focusing the miniaturized gear and clamp images, and the clamping strategy is used to obtain the miniaturized gear Z-directional information between the gear and clam and assemble the planetary gears. Experiments show the good performance of the algorithm and strategy.
Three typical path planning methods, i.e. artificial potential field, probabilistic path planning and biologically inspired neural network, were introduced. The analysis and comparisons were made in relating to genera...
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Three typical path planning methods, i.e. artificial potential field, probabilistic path planning and biologically inspired neural network, were introduced. The analysis and comparisons were made in relating to general aspects of the complexity, robustness and adaptability of the methods.
A central problem for implementing efficient quantum computing is how to realize fast operations (both one- and two-bit ones). However, this is difficult to achieve for a collection of qubits, especially for those sep...
A central problem for implementing efficient quantum computing is how to realize fast operations (both one- and two-bit ones). However, this is difficult to achieve for a collection of qubits, especially for those separated far away, because the interbit coupling is usually much weaker than the intrabit coupling. Here we present an experimentally feasible method to effectively couple two flux qubits via a common inductance and treat both single and coupled flux qubits with more realistic models, which include the loop inductance. The main advantage of our proposal is that a strong interbit coupling can be achieved using a small inductance, so that two-bit as fast as one-bit operations can be easily realized. We also show the flux dependence of the transitions between states for the coupled flux qubits.
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