A New Design of $H$ -Infinity Piecewise Filtering for Discrete-Time Nonlinear Time-Varying Delay Systems via T–S Fuzzy Affine Models

Wei, Yanling; Qiu, Jianbin; Shi, Peng; Lam, Hak‐Keung

doi:10.1109/tsmc.2016.2598785

Cited by 120 publications

(53 citation statements)

References 46 publications

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“…SOM is one of the most widely used unsupervised neural network algorithms successfully applied for hyperspectral image classification [29,36,37] and data visualization [38]. Alternative approaches include rule base fuzzy logic [39][40][41] and Markovian jump systems [42,43] which could be combined with ANN for switching decision making.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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“…Lemma 6 (Wirtinger-based inequality [29,30]). For a given symmetric positive definite matrix ∈ S + , any sequence of discrete-time variable in [−ℎ, 0] ∩ Z → R , where ℎ ≥ 1, the following inequality holds:…”

Section: Complexitymentioning

confidence: 99%

Improved Reduced‐Order Fault Detection Filter Design for Polytopic Uncertain Discrete‐Time Markovian Jump Systems with Time‐Varying Delays

et al. 2018

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The fault detection (FD) reduced-order filtering problem is investigated for a family of polytopic uncertain discrete-time Markovian jump linear systems (MJLSs) with time-varying delays. Under meeting the control precision requirements of the complex systems, the reduced-order fault detection filter can improve the efficiency of the fault detection. Then, by the aid of the Markovian Lyapunov function and convex polyhedron techniques, some novel time-varying delays and polytopic uncertain sufficient conditions in terms of linear matrix inequality (LMI) are proposed to insure the existence of the FD reduced-order filter. Finally, an illustrative example is provided to verify the usefulness of the given method.

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“…Removing noise in an image is important for improving image quality. Some filters like H-infinity filter have been applied to practical industrial systems [2][3][4]. Usually, noise originates from taking pictures through a defective sensor or transmitting images through a noisy channel [5].…”

Section: Introductionmentioning

confidence: 99%

An adaptive dynamically weighted median filter for impulse noise removal

Khan

Lee

2017

EURASIP J. Adv. Signal Process.

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A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

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A New Design of $H$ -Infinity Piecewise Filtering for Discrete-Time Nonlinear Time-Varying Delay Systems via T–S Fuzzy Affine Models

Cited by 120 publications

References 46 publications

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Improved Reduced‐Order Fault Detection Filter Design for Polytopic Uncertain Discrete‐Time Markovian Jump Systems with Time‐Varying Delays

An adaptive dynamically weighted median filter for impulse noise removal

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