On a nonlinear iterative method in applied mechanics, part II

1982 ◽  
Vol 30 (3) ◽  
pp. 323-333 ◽  
Author(s):  
G.F. Carey ◽  
R. Krishnan
Keyword(s):  
Iterative Method ◽  
Applied Mechanics ◽  
Nonlinear Iterative Method

A new nonlinear iterative method for SPN theory

2017 ◽  
Vol 110 ◽  
pp. 920-927 ◽  
Author(s):  
Qingquan Pan ◽  
Haoliang Lu ◽  
Dongsheng Li ◽  
Kan Wang
Keyword(s):  
Iterative Method ◽  
Nonlinear Iterative Method

scholarly journals Length-weight relationships of the ichthyofauna associated with the Brazilian sardine, Sardinella brasiliensis, on the Southeastern Brazilian Bight (22°S-29°S) between 2008 and 2010

2013 ◽  
Vol 13 (2) ◽  
pp. 326-330 ◽  
Author(s):  
André Martins Vaz-dos-Santos ◽  
Carmen Lúcia Del Bianco Rossi-Wongtschowski
Keyword(s):  
Iterative Method ◽  
Least Squares ◽  
Residual Analysis ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Method Of Least Squares ◽  
Positive Allometry ◽  
B Values ◽  
Acoustic Methods ◽  
Nonlinear Iterative Method

In this study, estimates of length-weight relationships are presented for twenty-four species caught in association with the Brazilian sardine, Sardinella brasiliensis, during four acoustics surveys carried out under the Program ECOSAR (Prospecting and evaluation of biomass of the stock of Brazilian sardine on the southeast coast by acoustic methods), which was to evaluate the biomass of species that were caught. The model parameters were estimated with the nonlinear iterative method of least squares. The value of the coefficient of determination (r2) and residual analysis were employed to verify the appropriateness of fit. The coefficient b values were tested with respect to isometry (β=3) using a tα1,0.05 test. The values of coefficient b ranged from 2.377 to 3.538. There is a tendency for positive allometry (b) in the sampled ichythyocenose.

THE REALIZATION OF THE ITERATIVE METHOD OF THE LEAST SQUARES FOR THE ESTIMATION OF STATIC OBJECT PARAMETERS IN MATLAB ENVIRONMENT

2017 ◽  
pp. 28-36
Author(s):  
Galina Vasil’evna Troshina ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Parameter Estimation ◽  
Iterative Method ◽  
Least Squares ◽  
Input Signal ◽  
Layer Structure ◽  
Estimation Procedure ◽  
Parameter Estimation Procedure ◽  
Noise Simulation ◽  
Static Object ◽  
Object Parameters

It was suggested to use the system model working in real time for an iterative method of the parameter estimation. It gives the chance to select a suitable input signal, and also to carry out the setup of the object parameters. The object modeling for a case when the system isn't affected by the measurement noises, and also for a case when an object is under the gaussian noise was executed in the MatLab environment. The superposition of two meanders with different periods and single amplitude is used as an input signal. The model represents the three-layer structure in the MatLab environment. On the most upper layer there are units corresponding to the simulation of an input signal, directly the object, the unit of the noise simulation and the unit for the parameter estimation. The second and the third layers correspond to the simulation of the iterative method of the least squares. The diagrams of the input and the output signals in the absence of noise and in the presence of noise are shown. The results of parameter estimation of a static object are given. According to the results of modeling, the algorithm works well even in the presence of significant measurement noise. To verify the correctness of the work of an algorithm the auxiliary computations have been performed and the diagrams of the gain behavior amount which is used in the parameter estimation procedure have been constructed. The entry conditions which are necessary for the work of an iterative method of the least squares are specified. The understanding of this algorithm functioning principles is a basis for its subsequent use for the parameter estimation of the multi-channel dynamic objects.

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