Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm

2019 ◽  
Vol 124 (1272) ◽  
pp. 271-295 ◽  
Author(s):  
H. O. Verma ◽  
N. K. Peyada
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Computational Cost ◽  
Feedforward Neural Network ◽  
Estimation Methods ◽  
Flight Data ◽  
Regression Methods ◽  
Directional Motion ◽  
Aerodynamic Parameters ◽  
Learning Machine

ABSTRACTThe research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

Predicting Surface Roughness in Turning Operation Using Extreme Learning Machine

2014 ◽  
Vol 554 ◽  
pp. 431-435 ◽  
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Extreme Learning Machine ◽  
Cutting Parameters ◽  
Feedforward Neural Network ◽  
Support Vector ◽  
Satisfactory Solution ◽  
Modeling Techniques ◽  
Learning Machine ◽  
Hidden Layer

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods

2020 ◽  
pp. 1-21
Author(s):  
H.O. Verma ◽  
N.K. Peyada
Keyword(s):  
Maximum Likelihood ◽  
Extreme Learning Machine ◽  
Unsteady Aerodynamics ◽  
Estimation Algorithm ◽  
Neural Model ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Aerodynamic Parameters ◽  
Learning Machine ◽  
And Control

ABSTRACT The stability and control derivatives are essential parameters in the flight operation of aircraft, and their determination is a routine task using classical parameter estimation methods based on maximum likelihood and least-squares principles. At high angle-of-attack, the unsteady aerodynamics may pose difficulty in aerodynamic structure determination, hence data-driven methods based on artificial neural networks could be an alternative choice for building models to characterise the behaviour of the system based on the measured motion and control variables. This research paper investigates the feasibility of using a recurrent neural model based on an extreme learning machine network in the modelling of the aircraft dynamics in a restricted sense for identification of the aerodynamic parameters. The recurrent extreme learning machine network is combined with the Gauss–Newton method to optimise the unknowns of the postulated aerodynamic model. The efficacy of the proposed estimation algorithm is studied using real flight data from a quasi-steady stall manoeuvre. Furthermore, the estimates are validated against the parameters estimated using the maximum likelihood method. The standard deviations of the estimates demonstrate the effectiveness of the proposed algorithm. Finally, the quantities regenerated using the estimates present good agreement with their corresponding measured values, confirming that a qualitative estimation can be obtained using the proposed estimation algorithm.

Hierarchical extreme learning machine for feedforward neural network

2014 ◽  
Vol 128 ◽  
pp. 128-135 ◽  
Author(s):  
Hong-Gui Han ◽  
Li-Dan Wang ◽  
Jun-Fei Qiao
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Feedforward Neural Network ◽  
Learning Machine

scholarly journals Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

2014 ◽  
Vol 129 ◽  
pp. 428-436 ◽  
Author(s):  
Tiago Matias ◽  
Francisco Souza ◽  
Rui Araújo ◽  
Carlos Henggeler Antunes
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Feedforward Neural Network ◽  
Learning Machine ◽  
Hidden Layer

Aircraft parameter estimation using ELM network

2020 ◽  
Vol 92 (6) ◽  
pp. 895-907
Author(s):  
Hari Om Verma ◽  
Naba Kumar Peyada
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Mathematical Formulation ◽  
Estimation Methods ◽  
Content Type ◽  
Flight Data ◽  
The Real ◽  
Aerodynamic Parameters ◽  
Hidden Layer ◽  
Error Method

Purpose The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network. Design/methodology/approach The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed-forward neural network (FFNN) known as extreme learning machine (ELM), which overcomes the issues such as learning rate, number of epochs, local minima, generalization performance and computational cost, as encountered in the conventional gradient learning-based FFNN has been used for the nonlinear modeling of the aerodynamic forces and moments. A mathematical formulation based on the partial differentiation is proposed to estimate the aerodynamic parameters. Findings The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters using the proposed methodology. The efficacy of the estimates is verified with the results obtained through the conventional parameter estimation methods such as the equation-error method and filter-error method. Originality/value The present study is an outcome of the research conducted on ELM for the estimation of aerodynamic parameters from the real flight data. The proposed method is capable to estimate the parameters in the presence of noise.

scholarly journals Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Wu ◽  
Xiaojuan Chen ◽  
Zhaohua Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Finite Impulse Response ◽  
Feedforward Neural Network ◽  
System Stability ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

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