Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model

2016 ◽  
Vol 6 (1) ◽  
pp. 167 ◽  
Author(s):  
A Brouri ◽  
S Slassi
Keyword(s):  
Nonlinear Systems ◽  
Identification Problem ◽  
Hammerstein Model ◽  
Asymptotically Stable ◽  
Two Stage ◽  
Series Connection ◽  
Static Nonlinearity ◽  
Identification Method ◽  
Frequency Identification ◽  
Nonlinear Static

Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

scholarly journals Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model

2016 ◽  
Vol 6 (1) ◽  
pp. 167
Author(s):  
A Brouri ◽  
S Slassi
Keyword(s):  
Nonlinear Systems ◽  
Identification Problem ◽  
Hammerstein Model ◽  
Asymptotically Stable ◽  
Two Stage ◽  
Series Connection ◽  
Static Nonlinearity ◽  
Identification Method ◽  
Frequency Identification ◽  
Nonlinear Static

Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

scholarly journals Dog Identification Method Based on Muzzle Pattern Image

2020 ◽  
Vol 10 (24) ◽  
pp. 8994
Author(s):  
Dong-Hwa Jang ◽  
Kyeong-Seok Kwon ◽  
Jung-Kon Kim ◽  
Ka-Young Yang ◽  
Jong-Bok Kim
Keyword(s):  
Feature Extraction ◽  
Radio Frequency Identification ◽  
Hamming Distance ◽  
Histogram Equalization ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Identification Method ◽  
Approximate Nearest Neighbors ◽  
Frequency Identification ◽  
Scale Invariant Feature

Currently, invasive and external radio frequency identification (RFID) devices and pet tags are widely used for dog identification. However, social problems such as abandoning and losing dogs are constantly increasing. A more effective alternative to the existing identification method is required and the biometrics can be the alternative. This paper proposes an effective dog muzzle recognition method to identify individual dogs. The proposed method consists of preprocessing, feature extraction, matching, and postprocessing. For preprocessing, proposed resize and histogram equalization are used. For feature extraction algorithm, Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Binary Robust Invariant Scaling Keypoints (BRISK) and Oriented FAST, and Rotated BRIEF (ORB) are applied and compared. For matching, Fast Library for Approximate Nearest Neighbors (FLANN) is used for SIFT and SURF, and hamming distance are used for BRISK and ORB. For postprocessing, two techniques to reduce incorrect matches are proposed. The proposed method was evaluated with 55 dog muzzle pattern images acquired from 11 dogs and 990 images augmented by the image deformation (i.e., angle, illumination, noise, affine transform). The best Equal Error Rate (EER) of the proposed method was 0.35%, and ORB was the most appropriate for the dog muzzle pattern recognition.

Electromagnetic and thermal parameter identification method for best prediction of temperature distribution on transformer tank covers

2015 ◽  
Vol 34 (2) ◽  
pp. 485-495 ◽  
Author(s):  
Patricia Penabad Durán ◽  
Paolo Di Barba ◽  
Xose Lopez-Fernandez ◽  
Janusz Turowski
Keyword(s):  
Temperature Distribution ◽  
Measurement Error ◽  
Parameter Identification ◽  
Objective Function ◽  
Measurement Errors ◽  
Optimization Algorithms ◽  
Identification Problem ◽  
Content Type ◽  
Identification Method ◽  
Wide Range

Purpose – The purpose of this paper is to describe a parameter identification method based on multiobjective (MO) deterministic and non-deterministic optimization algorithms to compute the temperature distribution on transformer tank covers. Design/methodology/approach – The strategy for implementing the parameter identification process consists of three main steps. The first step is to define the most appropriate objective function and the identification problem is solved for the chosen parameters using single-objective (SO) optimization algorithms. Then sensitivity to measurement error of the computational model is assessed and finally it is included as an additional objective function, making the identification problem a MO one. Findings – Computations with identified/optimal parameters yield accurate results for a wide range of current values and different conductor arrangements. From the numerical solution of the temperature field, decisions on dimensions and materials can be taken to avoid overheating on transformer covers. Research limitations/implications – The accuracy of the model depends on its parameters, such as heat exchange coefficients and material properties, which are difficult to determine from formulae or from the literature. Thus the goal of the presented technique is to achieve the best possible agreement between measured and numerically calculated temperature values. Originality/value – Differing from previous works found in the literature, sensitivity to measurement error is considered in the parameter identification technique as an additional objective function. Thus, solutions less sensitive to measurement errors at the expenses of a degradation in accuracy are identified by means of MO optimization algorithms.

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