Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas ...
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Neural gas network is a single-layered soft competitive neural network, which can be applied to clustering analysis with fast convergent speed comparing to Self-organizing Map (SOM), K-means etc. Combining neural gas with principal component analysis, this paper proposes a new clustering method, namely principal components analysis neural gas (PCA-NG), and the online learning algorithm is also given. The soft competitive learning of PCA-NG is based on local principal subspace, which characterizes the profile of a certain cluster. We utilize the PCA-NG to the domain of intrusion detection. Some experiments are carried out to illustrate the performance of the proposed approach by using a synthetic Gaussian-distributed dataset and the KDD CUP 1999 Intrusion Detection Evaluation dataset.
To characterize bound entangled states is one of the most challenging problems in quantum information theory. In this paper, we look from a new different angle to find bound entangled states. In particularly, we give ...
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To characterize bound entangled states is one of the most challenging problems in quantum information theory. In this paper, we look from a new different angle to find bound entangled states. In particularly, we give a general scheme for construction of new bound entangled states from known bound entangled states by convex linear combination. This is achieved by quantitatively characterizing entanglement of quantum states via the positive partial transpose (PPT) criterion and the computable cross-norm or realignment (CCNR) criterion. The obtained results are illustrated through an explicit example.
We present two unifying formulations of deterministic automata, probabilistic automata, fuzzy automata, and quantum automata. Based on the formulations, the power of recognizing languages of these automaton models is ...
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We present two unifying formulations of deterministic automata, probabilistic automata, fuzzy automata, and quantum automata. Based on the formulations, the power of recognizing languages of these automaton models is compared and the generalization of automaton models is considered.
In May 2010, the third IOI workshop took place in Schloss Dagstuhl, Germany. It was motivated by the discussions held at and after the panel session of 2009's IOI conference in Plovdiv. There, discussions focussed...
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Circularly polarized electric fields incident on subwavelength apertures produce near-field phase singularities with phase vorticity ±1 depending on the polarization handedness. These near-field phase singulariti...
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Circularly polarized electric fields incident on subwavelength apertures produce near-field phase singularities with phase vorticity ±1 depending on the polarization handedness. These near-field phase singularities combine with those associated with orbital angular momentum and result in polarization-dependent transmission. We produce arbitrary phase vorticity in the longitudinal component of scattered electric fields by varying the incident beam and aperture configuration.
The Smarandachely adjacent-vertex distinguishing total coloring of graphs is a proper k-total coloring such that every adjacent vertex coloring set not embrace each other, the minimal number k is denoted the Smarandac...
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The Smarandachely adjacent-vertex distinguishing total coloring of graphs is a proper k-total coloring such that every adjacent vertex coloring set not embrace each other, the minimal number k is denoted the Smarandachely adjacent-vertex distinguishing total coloring chromatic number of graphs. Where the coloring set include the colors of all edges incident to the vertex plus the color of it. In this paper, we construct two kind of 3-regular graph R n 3 and S 4n 3 , and obtain the Smarandachely adjacent-vertex distinguishing total coloring chromatic number of it.
The discrete-time uncertain system with time delay is investigated for bounded input bounded output (BIBO). By constructing an augmented Lyapunov function, three different sufficient conditions are established for BIB...
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The discrete-time uncertain system with time delay is investigated for bounded input bounded output (BIBO). By constructing an augmented Lyapunov function, three different sufficient conditions are established for BIBO stabilization. These conditions are expressed in the form of linear matrix inequalities (LMIs), whose feasibility can be easily checked by sing Matlab LMI Toolbox. Two numerical examples are provided to demonstrate the effectiveness of the derived results.
In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some ...
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In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.
An image reconstruction algorithm using compressed sensing (CS) with deterministic matrices of second-order Reed-Muller (RM) sequences is introduced. The 1D algorithm of Howard et al. using CS with RM sequences suffer...
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ISBN:
(纸本)9781424442959;9781424442966
An image reconstruction algorithm using compressed sensing (CS) with deterministic matrices of second-order Reed-Muller (RM) sequences is introduced. The 1D algorithm of Howard et al. using CS with RM sequences suffers significant loss in speed and accuracy when the degree of sparsity is not high, making it inviable for 2D signals. This paper describes an efficient 2D CS algorithm using RM sequences, provides medical image reconstruction examples, and compares it with the original 2D CS using noiselets. This algorithm entails several innovations that enhance its suitability for images: initial best approximation, a greedy algorithm for the nonzero locations, and a new approach in the least-squares step. These enhancements improve fidelity, execution time, and stability in the context of image reconstruction.
In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some n...
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In this paper, the robust exponential stability problem of uncertain discrete-time recurrent neural networks with timevarying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii function, some new improved stability criteria are obtained in forms of linear matrix inequality (LMI). Compared with some recent results in literature, the conservatism of the new criteria is reduced notably. Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.
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