3,588 research outputs found
Variable neural networks for adaptive control of nonlinear systems
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example
Likelihood Analysis of Power Spectra and Generalized Moment Problems
We develop an approach to spectral estimation that has been advocated by
Ferrante, Masiero and Pavon and, in the context of the scalar-valued covariance
extension problem, by Enqvist and Karlsson. The aim is to determine the power
spectrum that is consistent with given moments and minimizes the relative
entropy between the probability law of the underlying Gaussian stochastic
process to that of a prior. The approach is analogous to the framework of
earlier work by Byrnes, Georgiou and Lindquist and can also be viewed as a
generalization of the classical work by Burg and Jaynes on the maximum entropy
method. In the present paper we present a new fast algorithm in the general
case (i.e., for general Gaussian priors) and show that for priors with a
specific structure the solution can be given in closed form.Comment: 17 pages, 4 figure
Extending the functional equivalence of radial basis functionnetworks and fuzzy inference systems
We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result
The relaxation method for learning in artificial neural networks
A new mathematical approach for deriving learning algorithms for various neural network models including the Hopfield model, Bidirectional Associative Memory, Dynamic Heteroassociative Neural Memory, and Radial Basis Function Networks is presented. The mathematical approach is based on the relaxation method for solving systems of linear inequalities. The newly developed learning algorithms are fast and they guarantee convergence to a solution in a finite number of steps. The new algorithms are highly insensitive to choice of parameters and the initial set of weights. They also exhibit high scalability on binary random patterns. Rigorous mathematical foundations for the new algorithms and their simulation studies are included
Parameterization of Stabilizing Linear Coherent Quantum Controllers
This paper is concerned with application of the classical Youla-Ku\v{c}era
parameterization to finding a set of linear coherent quantum controllers that
stabilize a linear quantum plant. The plant and controller are assumed to
represent open quantum harmonic oscillators modelled by linear quantum
stochastic differential equations. The interconnections between the plant and
the controller are assumed to be established through quantum bosonic fields. In
this framework, conditions for the stabilization of a given linear quantum
plant via linear coherent quantum feedback are addressed using a stable
factorization approach. The class of stabilizing quantum controllers is
parameterized in the frequency domain. Also, this approach is used in order to
formulate coherent quantum weighted and control problems for
linear quantum systems in the frequency domain. Finally, a projected gradient
descent scheme is proposed to solve the coherent quantum weighted control
problem.Comment: 11 pages, 4 figures, a version of this paper is to appear in the
Proceedings of the 10th Asian Control Conference, Kota Kinabalu, Malaysia, 31
May - 3 June, 201
Denoising using Self Adaptive Radial Basis Function
This paper presents an adaptive form of the Radial basis function neural network to correct the noisy image in a unified way without estimating the existing noise model in the image. Proposed method needs a single noisy image to train the adaptive radial basis function network to learn the correction of the noisy image. The gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptiveness in the radial basis function network is compared with the fixed form of spreadness and the center value of kernel function. The proposed solution can correct the image suffered from different varieties of noises like speckle noise, Gaussian noise, salt & pepper noise separately or combination of noises. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter
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