The Charney-Hasegawa-Mima equation, with random forcing at the narrow band wave-number region, which is set to be slightly larger than the characteristic wave number λ, evaluating the inverse ion Larmor radius in pla...
The Charney-Hasegawa-Mima equation, with random forcing at the narrow band wave-number region, which is set to be slightly larger than the characteristic wave number λ, evaluating the inverse ion Larmor radius in plasma, is numerically studied. It is shown that the Fourier spectrum of the potential vorticity fluctuation in the development of turbulence with an initial condition of quiescent state obeys a dynamic scaling law for k≪λ. The dimensional analysis with the assumption that the energy transfer rate ε in the inverse cascade is constant with time leads to the scaling form S(k,t) =λ1/2ε5/4t7/4F(k/k-bar(t))[k-bar(t)∼λ3/4ε−1/8t−3/8] with a scaling function F(x), which turns out to be in good agreement with numerical experiments.
This paper formulates a necessary condition for multilayer nets to have solutions by a set of normal vectors orthogonal to separation hyperplanes. Comparing the necessary condition to the distributions of normal vecto...
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This paper formulates a necessary condition for multilayer nets to have solutions by a set of normal vectors orthogonal to separation hyperplanes. Comparing the necessary condition to the distributions of normal vectors with the weights and biases initialized ordinarily by random numbers with zero mean, it is derived that bipolar nets are superior to unipolar nets in convergence of the back propagation learning initialized in such an ordinary manner.
Solution for the XOR problem are formulated by normal vectors orthogonal to separation hyperplanes when nonlinear units are given by threshold functions. The shapes of solution regions are shown to be different for bi...
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This paper proposes a new approach to hidden-layer size reducing for multilayer neural networks, using the orthogonal least-squares (OLS) method based on the Gram-Schmidt orthogonal transformation. A neural network wi...
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ISBN:
(纸本)0780342089
This paper proposes a new approach to hidden-layer size reducing for multilayer neural networks, using the orthogonal least-squares (OLS) method based on the Gram-Schmidt orthogonal transformation. A neural network with a large hidden-layer size is first trained via a standard training rule. Then the OLS method is introduced to identify and eliminate redundant neurons such that a simpler neural network is obtained. The OLS method is employed as a forward regression procedure to select a suitable set of neurons from a large set of preliminarily trained hidden neurons, such that the input to the output-layer neuron is reconstructed with less hidden neurons. Simulation results are included to show the efficiency of the proposed method.
When building up a fuzzy diagnosis system, symptom parameters (SPs) must be extracted and the membership functions between the symptom parameters and failure categories must be defined for fuzzy inference. Currently, ...
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When building up a fuzzy diagnosis system, symptom parameters (SPs) must be extracted and the membership functions between the symptom parameters and failure categories must be defined for fuzzy inference. Currently, however, there is no acceptable method for extracting the optimum SP by which the failure types can be sensitively distinguished. In order to overcome this difficulty and ensure highly accurate failure diagnosis, in this paper, a new method called "sequential self-reorganization of symptom parameters" is proposed by using genetic algorithms (GA). Also the identification method of membership functions of symptom parameters is discussed by using the possibility theory. The efficiency of these methods is verified by applying them to a ball bearing diagnosis system. The new methods proposed here can also be applied to other pattern recognition problems.
It is well-known that the angle dependent disturbances in a servo motor caused by nonuniformity of field windings, armature cogging, rotor imbalance, nonuniform load etc., may influence the speed control performance g...
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It is well-known that the angle dependent disturbances in a servo motor caused by nonuniformity of field windings, armature cogging, rotor imbalance, nonuniform load etc., may influence the speed control performance greatly. This often leads to large speed fluctuations which are undesired in practical situations. Therefore speed fluctuation reduction techniques in the presence of angle dependent disturbances are strongly required and have been being challenged by a lot of researchers. In this paper, the authors propose a new approach to this problem via adaptive control with the aid of a radial basis function (RBF) network composed of gaussian basis functions. The angle dependent disturbances which are viewed as a periodic nonlinear function with a period of 2π[rad] in the angle-domain, is approximated by a RBF network in the domain of [0, 2π)[rad]. Then an adaptive linearization control system employing the RBF network which compensates the disturbances is proposed. The RBF network has the advantage that it is linear-in-parameter and hence the parameter adaption is very fast and easy to implement. It is proved through theoretical analysis that the stability of the adaptive control is guaranteed by the Lyapunov stability theory. Finally, simulational and experimental results are included in the paper to show the excellent performance of the proposed method.
This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a gro...
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This paper presents a model-based approach to fault detection of dynamic systems, which is robust to unmodeled dynamics. A “Quasi-ARMAX model᾿ is first proposed for describing nonlinear systems by incorporating a group of certain nonlinear structures into a linear ARMAX structure. The model can be used for a best linear approximation of the system, as well as for the estimation of resulting unmodeled dynamics, by a hierarchical implementation of recursive identification. Then robust fault detection is performed based on thresholding approach using Kullback discrimination information as fault detection index, in which the estimated unmodeled dynamics is incorporated.
Usually, the subspace-based state-space system identification algorithms are focused on discrete-time models, which may cause some numerical problems when the sampling interval is small. This paper proposes an algorit...
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Usually, the subspace-based state-space system identification algorithms are focused on discrete-time models, which may cause some numerical problems when the sampling interval is small. This paper proposes an algorithm of subspace-based state-space system identification for continuous-time systems from sampled input-output data. The ω — operator ω = ( p - α)/( p + α) where p denotes a differential operator and α > 0, is introduced to avoid direct numerical differentiations. And the ω — operator state-space model identified by the 4SID method can be transformed back to the common continuous-time state-space model. The numerical superiority of the ω — operator approach compared to some other methods is verified through simulation study.
This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi...
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This research is concerned with fault detection of nonlinear systems using Kullback discrimination information (KDI) as an index. A hybrid quasi-ARMAX model is proposed, which combines a linear ARMAX model and a multi-ARX-model based on interpolation. In the case where the faults occur on the ARMAX model part, a KDI-based "robust" fault detection is performed, in which multi-ARX-model part is treated as error due to nonlinear undermodeling. In other cases, the model is transformed into several local ARMAX models and fault detection is performed by using the KDI to discriminate each identified local model. In this paper, we mainly concentrate our discussion on the latter cases.
This paper deals with manipulation of a floating object by two space robots with manipulators. It is shown in this paper that a total system consisting of two robots and a floating object could be treated as a distrib...
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This paper deals with manipulation of a floating object by two space robots with manipulators. It is shown in this paper that a total system consisting of two robots and a floating object could be treated as a distributed system, and then a new generalized Jacobian matrix (GJM) is defined. Moreover, it is confirmed that this type of GJM is effective for using adaptive control for decreasing the amount of calculation for the control algorithm.
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