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## Nocl atom closest to negative side

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## Stable cross entropy

This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis This book takes the topic of H-infinity control as a point of departure, and pursues an improved controller design suggested in the mainstream of robust control. This brief presents a general discrete-time recurrent network for linear variational inequalities and related optimization problems with hybrid constraints.

In contrary to the existing discrete-time networks, this general model can operate not only on bound constraints, but also on hybrid constraints comprised of inequality, equality and bound constraints A transductive neuro-fuzzy controller: application to a drilling process. Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process.

The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system e Adaptive filter design using recurrent cerebellar model articulation controller.

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller CMAC.

The proposed locally recurrent globally feedforward recurrent CMAC RCMAC has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation.

In this paper, based on a one-neuron recurrent neural network, a novel k-winners-take-all k -WTA network is proposed.

Finite time convergence of the proposed neural network is proved using the Lyapunov method. The k-WTA operation is first converted equivalently into a linear programming problem. Discriminative semi-supervised feature selection via manifold regularization. Feature selection has attracted a huge amount of interest in both research and application communities of data mining.

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We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data Recently, the minimum error entropy MEE criterion has been used as an information theoretic alternative to traditional mean-square error criterion in supervised learning systems.

The weight update equation is formulated in the form of block-data Design of recurrent neural networks for solving constrained least absolute deviation problems.

Recurrent neural networks for solving constrained least absolute deviation LAD problems or L 1 -norm optimization problems have attracted much interest in recent years. But so far most neural networks can only deal with some special linear constraints efficiently. In this paper, two neural networks are proposed for solving LAD problems with various linear constraints including equality, two-sided inequality and bound constraints.

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When tailored to solve some special cases of LAD problems in which not all types of constraints are present, the two networks can yield simpler architectures than most existing ones in the literature An augmented LKF approach involving derivative information of both state and delay. An augmented Lyapunov-Krasovskii functional LKF approach is presented to derive sufficient conditions for the existence, uniqueness, and globally exponential stability of the equilibrium point of a class of cellular neural networks with time-varying delays. By dividing the variation interval of the time delay into several subintervals with equal length, a novel vector LKF is introduced and new conditions are obtained based on the homeomorphism mapping principle, free-weighting matrix method, and linear matrix inequality techniques Common asymptotic behavior of solutions and almost periodicity for discontinuous, delayed, and impulsive neural networks.

The paper considers a general neural network model with impulses at a given sequence of instants, discontinuous neuron activations, delays, and time-varying data and inputs. It is also shown, via a new technique based on prolonging the solutions of the delayed neural network to -infinity, that it is possible to select a unique special solution that is globally exponentially stable and can be considered as the unique global attractor for the network Nonlinear extension of Bode's integral based on an information-theoretic interpretation Guoqiang Zang , Pablo A.

Controller Reduction by Balanced Truncation D.

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Mustafa , Keith Glover. References Publications referenced by this paper. Limebeer , John C. Mustafa , M.

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Khargonekar andM. Entropy-minimizing and risk-sensitive control rules Peter Whittle.