The Parametric Approximation

2003 ◽  
pp. 149-164
Author(s):  
V. I. Shalashilin ◽  
E. B. Kuznetsov
Keyword(s):  
Parametric Approximation

Cognitive null steering in frequency diverse array radars

2015 ◽  
Vol 9 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Sarah Saeed ◽  
Ijaz Mansoor Qureshi ◽  
Abdul Basit ◽  
Ayesha Salman ◽  
Waseem Khan
Keyword(s):  
Feedback Loop ◽  
Super Resolution ◽  
Computation Time ◽  
Frequency Offset ◽  
Multiple Signal Classification ◽  
Location Parameters ◽  
Null Steering ◽  
Frequency Diverse Array ◽  
Null Placement ◽  
Parametric Approximation

Null steering has been a challenge in radar communications for the past few decades. In this paper, a novel cognitive null steering technique in frequency diverse array radars using frequency offset selection is presented. The proposed system is a complete implementable framework that provides precise and deep null placement in the range and angle locations of the interference source. The proposed system is cognitive such that the transmitter and receiver are connected via a feedback loop. System extracts interference source location parameters from the radar scene using Multiple Signal Classification, a super resolution direction of arrival estimation technique. Neural networks known for minimum computation time, and good non-linear and non-parametric approximation have been utilized for prediction of next location of the interference source. Simulation results validate the proposed frequency offset selection by demonstrating precise and deep nulls at the desired locations.

Class-Based Parametric Approximation to Histogram Equalization for ASR

2012 ◽  
Vol 19 (7) ◽  
pp. 415-418 ◽  
Author(s):  
Luz Garcia ◽  
Carmen Benítez Ortuzar ◽  
Angel De la Torre ◽  
Jose C. Segura
Keyword(s):  
Histogram Equalization ◽  
Parametric Approximation

scholarly journals Mathematical models and soil fertility management software

2020 ◽  
Vol 210 ◽  
pp. 04008
Author(s):  
Sergey Mitrofanov ◽  
Nikolay Novikov ◽  
Vasily Nikitin ◽  
Sergey Belykh
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Estimation Algorithm ◽  
Step Length ◽  
Descent Method ◽  
Soil Fertility Management ◽  
Problem Statement ◽  
Functions Of Two Variables ◽  
Microsoft Windows ◽  
Parametric Approximation

The article presents the results of studies on parametric approximation in spaces R2 (functions of one variable), R3 (functions of two variables) and Rn(n>3) (functions of three or more variables). Various classes of functions satisfying a priori conditions were studied: f(0, 0, 0)=0, $\mathop {\lim 1}\limits_{{x_i} \to + \infty } \,\,({x_1},\, \ldots ,\,{x_n}) = {c_i}$, ci = cont. Working algorithms and C/C++ software functioning in Microsoft Visual Studio 2019 system in Microsoft Windows 10 environment were developed. The main studies of the authors were aimed at developing effective computational algorithms for constructing approximating functions of two variables from various given classes of three-dimensional data samples (three-dimensional interconnected time series). The article provides a detailed description of the problem statement, introduces classes of approximating functions, provides algorithms for estimating the parameters of approximating functions and a description of the software. The estimation algorithm considered in the article is constructed according to the scheme of the coordinate descent method with optimization of the step length (Gauss-Seidel method).

scholarly journals Multiagent reinforcement learning using Non-Parametric Approximation

Respuestas ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 53-61
Author(s):  
David Luviano Cruz ◽  
Francesco José García Luna ◽  
Luis Asunción Pérez Domínguez
Keyword(s):  
Reinforcement Learning ◽  
Hybrid Control ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Generation Task ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Optimal Set ◽  
Parametric Approximation

This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.

Dispersion der gekoppelten Wellen beim stimulierten Raman-Effekt in einfachen piezoelektrischen kubischen Kristallen

1979 ◽  
Vol 34 (2) ◽  
pp. 126-133
Author(s):  
L. Merten ◽  
J. Wenk
Keyword(s):  
Pump Wave ◽  
Dispersion Curves ◽  
Stokes Component ◽  
Piezoelectric Crystals ◽  
Stokes Waves ◽  
Explicit Formulae ◽  
Anti Stokes ◽  
Polariton Branch ◽  
Parametric Approximation

Abstract Even in the most simple case of cubic piezoelectric crystals with only one polariton branch the problem of coupling of polariton, Stokes and anti-Stokes waves in the parametric approximation leads to six coupled waves, each consisting of a polariton component, a Stokes component and an anti-Stokes component. The dispersion of all six coupled waves can be described by explicit formulae in very good approximation. The shift of the dispersion curves of the coupled waves against the dispersion curves of the uncoupled waves is proportional to the square of the amplitude of the pump wave

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