We use molecular dynamics (MD) and particle-in-cell (PIC) simulation methods to investigate the dispersion relation of dust-acoustic waves in a one-dimensional, strongly coupled (Coulomb coupling parameter=Γ=ratio of...
We use molecular dynamics (MD) and particle-in-cell (PIC) simulation methods to investigate the dispersion relation of dust-acoustic waves in a one-dimensional, strongly coupled (Coulomb coupling parameter=Γ=ratio of the Coulomb energy to the thermal energy=120 ) dusty plasma. We study both cases where the dust is represented by a small number of simulation particles that form into a regular array structure (crystal limit) as well as where the dust is represented by a much larger number of particles (fluid limit).
Nonsymmetrical effective interactions between dust grains in dusty plasmas can arise from a variety of mechanisms such as nonuniform charging, ion focusing and wakes, and induced grain polarization. These effects can ...
Nonsymmetrical effective interactions between dust grains in dusty plasmas can arise from a variety of mechanisms such as nonuniform charging, ion focusing and wakes, and induced grain polarization. These effects can be included by describing the total effective interaction in terms of its first two multipoles: the monopole term which corresponds to the usual screened Coulomb interaction, and the dipole term which can be either attractive or repulsive depending on the relative orientation between grains. We consider dust waves propagating along the dipole axis in such a system (in its crystalline phase) and dispersion relations are presented.
This paper presents an approach to the design of a framework for an adaptive approximate time rough controller. The class of rough control systems described in this paper utilizes a new form of clock called an approxi...
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We discuss a variety of adaptive critic designs (ACD's) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic p...
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We discuss a variety of adaptive critic designs (ACD's) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: Heuristic dynamic programming (HDP), dual heuristic programming (DHP), and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACD's. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACD's. This leads to a generalized training procedure for ACD's.
Reinforcement learning is an integral part of intelligent agent research. The development of this field, however, has been largely independent of the latest developments in neural networks. As a result, the most popul...
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ISBN:
(纸本)0780341236
Reinforcement learning is an integral part of intelligent agent research. The development of this field, however, has been largely independent of the latest developments in neural networks. As a result, the most popular designs for intelligent agents utilize neural network architectures from several years ago. This article recommends newer, proven designs for reinforcement learning. The recommended designs share historical roofs with the most popular architectures in place today, allowing improved performance without radical redesign of existing agents.
We present a procedure for obtaining derivatives used in training a recurrent network that combines in a unified framework the techniques of backpropagation through time and derivative adaptive critics. The resulting ...
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ISBN:
(纸本)0780341236
We present a procedure for obtaining derivatives used in training a recurrent network that combines in a unified framework the techniques of backpropagation through time and derivative adaptive critics. The resulting formulation is consistent with previous descriptions, but has the advantage of allowing the mentioned techniques to be used together in a proportion that is appropriate to a given problem.
We solve the problem: how to determine maximal allowable errors, possible for signals and parameters of each element of a network, proceeding from the condition that the vector of output signals of the network should ...
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This paper introduces an information granularity reduction principle in connection with the analysis of the component of uncertainty associated with data. This overall study is illustrated utilizing simple numerical s...
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This paper introduces an information granularity reduction principle in connection with the analysis of the component of uncertainty associated with data. This overall study is illustrated utilizing simple numerical studies dealing with dynamical systems with first order dynamics. Classical and fuzzy Petri models are introduced in the analysis of dynamical systems. The overall study is illustrated utilizing simple numeric studies. The agenda involves a number of essential development issues: (i) providing a constructive way to build Petri nets out of numerical experimental data from dynamical systems, (ii) analyzing the component of uncertainty associated with data and elaborating on its minimization via an optimal quantization of the variables involved in the model of construction, (iii) considering the role of set-theoretic and fuzzy set frameworks in the transformation of numeric quantities into their qualitative (symbolic) counterparts, and (iv) identifying the role of Petri nets in the analysis of dynamical systems.
Neural networks based on construction of orthogonal projectors in the tensor power of space of signals are described. A sharp estimate of their ultimate information capacity is obtained. The number of stored prototype...
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ISBN:
(纸本)0780341236
Neural networks based on construction of orthogonal projectors in the tensor power of space of signals are described. A sharp estimate of their ultimate information capacity is obtained. The number of stored prototype patterns (prototypes) can many times exceed the number of neurons. A comparison with the error control codes is made.
We propose a simple framework for critic-based training of recurrent neural networks and feedback controllers. We term the critics that are used primitive adaptive critics, since we represent them with the simplest po...
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