A comparison of neural networks, non-linear biased regression and a genetic algorithm for dynamic model identification

1995 ◽  
Vol 30 (1) ◽  
pp. 81-89 ◽  
Author(s):  
Barry M. Wise ◽  
Bradley R. Holt ◽  
Neal B. Gallagher ◽  
Samuel Lee
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Dynamic Model ◽  
Model Identification ◽  
Non Linear ◽  
Biased Regression

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

Rotorcraft control response using linearised and non-linear flight dynamic models with different inflow models

2017 ◽  
Vol 121 (1238) ◽  
pp. 553-575 ◽  
Author(s):  
T. Sakthivel ◽  
C. Venkatesan
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Model Identification ◽  
Flight Test ◽  
Step Size ◽  
Response Characteristics ◽  
Wide Range ◽  
Non Linear ◽  
Steady Level ◽  
Control Response

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

The Robot Wrist Sensor Dynamic Mode Building Method Based on Genetic Wavelet Neural Networks

2013 ◽  
Vol 455 ◽  
pp. 389-394
Author(s):  
A Long Yu ◽  
Jin Qiao Dai
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Dynamic Model ◽  
Force Sensor ◽  
Dynamic Mode ◽  
Improved Genetic Algorithm ◽  
Wavelet Neural Networks ◽  
Robot Wrist ◽  
New Type ◽  
Set Up

A kind of new dynamic modeling method is presented based on improved genetic algorithm (IGA) and wavelet neural networks (WNN) and the principle of algorithm is introduced for a new type robot wrist force sensor. The dynamic model of the wrist force sensor is set up according to data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic model are optimized by genetic algorithm. The results show that the proposed method can overcome the shortcomings of easy convergence to the local minimum points of BP algorithm, and the network complexity, the convergence and the generalization ability are well compromised and the training speed and precision of model are increased.

scholarly journals Genetic Algorithm Coupled with Neural Networks to Guesstimate the Subsurface Features of the Earth

2020 ◽  
Vol 1 (3) ◽  
pp. 13-27
Author(s):  
A. Stanley Raj ◽  
Y. Srinivas ◽  
R. Damodharan ◽  
B. Chendhoor ◽  
M. Sanjay Vimal
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soft Computing ◽  
Surface Characteristics ◽  
Advantages And Disadvantages ◽  
Resistivity Method ◽  
The Earth ◽  
Non Linear ◽  
Artificial Neural

Electrical resistivity method is often used to estimate the subsurface structure of the earth. Many inversion algorithms are available to estimate the subsurface features. However, predicting the exact parameter in the non-linear subsurface of the earth is difficult because of its complex composition. Soft computing tools can approximate the subsurface parameters more clearly. Each soft computing tool has certain advantages and disadvantages. A hybrid formation of algorithms will make the decision more appropriate than depending on a single tool. Here in our study the data obtained through Vertical Electrical Sounding has been used to determine the sub surface characteristics of earth viz., true resistivity and thickness. Artificial Neural Networks (ANN) requires certain optimizing procedures. Here in this paper, Genetic Algorithm (GA) is applied to optimize Artificial Neural Networks (ANN). This coupled approach is tested with the field data. Error percentage of algorithm nearly mimics the behavior of earth and is verified. The best performance result shows that this technique can be implemented to estimate the non-linear characteristics of the earth more noticeably.

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