scholarly journals Exploration on Robustness of Exponentially Global Stability of Recurrent Neural Networks with Neutral Terms and Generalized Piecewise Constant Arguments

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenxiao Si ◽  
Tao Xie ◽  
Biwen Li
Keyword(s):  
Global Stability ◽  
Signal Transmission ◽  
Original System ◽  
Interval Length ◽  
Piecewise Constant Arguments ◽  
Piecewise Constant ◽  
Compression Coefficient ◽  
Transmission Process ◽  
Exponentially Stable ◽  
The Given

With a view to the interference of piecewise constant arguments (PCAs) and neutral terms (NTs) to the original system and the significant applications in the signal transmission process, we explore the robustness of the exponentially global stability (EGS) of recurrent neural network (RNN) with PCAs and NTs (NPRNN). The following challenges arise: what the range of PCAs and the scope of NTs can NPRNN tolerate to be exponentially stable. So we derive two important indicators: maximum interval length of PCAs and the scope of neutral term (NT) compression coefficient here for NPRNN to be exponentially stable. Additionally, we theoretically proved that if the interval length of PCAs and the bound of NT compression coefficient are all lower than the given results herein, the disturbed NPRNN will still remain global exponential stability. Finally, there are two numerical examples to verify the deduced results’ effectiveness here.

Identification of Parameters in Hereditary Systems

1995 ◽  
Author(s):  
Ferenc Hartung ◽  
Janos Turi ◽  
Terry L. Herdman
Keyword(s):  
Parameter Identification ◽  
Delay Equations ◽  
Neutral Equations ◽  
Piecewise Constant Arguments ◽  
Piecewise Constant ◽  
Identification Of Parameters ◽  
State Dependent ◽  
Identification Technique ◽  
Numerical Performance

Abstract In this paper we study the numerical performance of a parameter identification technique, based on approximation by equations with piecewise constant arguments, on various classes of hereditary systems. The examples considered here include delay equations with state-dependent delays and neutral equations.

scholarly journals Research and Analysis of Digital Baseband Transmission System Based on MATLAB

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Chai Jianwen
Keyword(s):  
Signal Transmission ◽  
Digital Signal ◽  
Transmission System ◽  
Simulation Software ◽  
Line Pattern ◽  
Matlab Simulation ◽  
Simulation Process ◽  
Transmission Process ◽  
Baseband Signal ◽  
Digital Baseband

This paper mainly studies the basic concept of baseband transmission of digital signal and the transmission processof digital signal baseband transmission and how to design digital baseband transmission system with MATLABsoftware simulation. This paper fi rst introduces the theoretical basis of this subject, including digital communication,the composition of digital baseband transmission system and digital baseband signal transmission process. Then thepaper introduced the characteristics of digital baseband transmission system, including digital PAM signal powerdensity and common line pattern, and by comparing choosing the fi nal bipolar zero return code. Then we introducedthe MATLAB simulation software. The conditions of the best reception of the digital baseband signal are describedand how the waveform of the baseband signal is observed by an oscilloscope. Finally, according to the basic stepsof simulation process, the simulation process of digital baseband transmission system is realized by MATLABsimulation tool, and the system is analyzed.

scholarly journals Spatially Adaptive Regularization in Image Segmentation

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 226
Author(s):  
Laura Antonelli ◽  
Valentina De Simone ◽  
Daniela di Serafino
Keyword(s):  
Image Segmentation ◽  
Split Bregman ◽  
Minimization Method ◽  
Piecewise Constant ◽  
Median Filters ◽  
Spatial Image ◽  
Local Regularization ◽  
Spatial Features ◽  
Spatially Adaptive ◽  
The Given

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.

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