scholarly journals Data-Based Control for Humanoid Robots Using Support Vector Regression, Fuzzy Logic, and Cubature Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Liyang Wang ◽  
Ming Chen ◽  
Gai Li ◽  
Yongqing Fan
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Simulated Data ◽  
Stability Margin ◽  
Humanoid Robots ◽  
Fuzzy Membership Function ◽  
Support Vector ◽  
External Disturbances ◽  
Cubature Kalman Filter ◽  
The Stability

Time-varying external disturbances cause instability of humanoid robots or even tip robots over. In this work, a trapezoidal fuzzy least squares support vector regression- (TF-LSSVR-) based control system is proposed to learn the external disturbances and increase the zero-moment-point (ZMP) stability margin of humanoid robots. First, the humanoid states and the corresponding control torques of the joints for training the controller are collected by implementing simulation experiments. Secondly, a TF-LSSVR with a time-related trapezoidal fuzzy membership function (TFMF) is proposed to train the controller using the simulated data. Thirdly, the parameters of the proposed TF-LSSVR are updated using a cubature Kalman filter (CKF). Simulation results are provided. The proposed method is shown to be effective in learning and adapting occasional external disturbances and ensuring the stability margin of the robot.

scholarly journals Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hailun Wang ◽  
Daxing Xu
Keyword(s):  
Kalman Filter ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Unscented Kalman Filter ◽  
Selection Method ◽  
Parameter Selection ◽  
Support Vector ◽  
Regression Parameters ◽  
Cubature Kalman Filter ◽  
Adaptive Fusion

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

A novel method based on least squares support vector regression combing with strong tracking particle filter for machinery condition prognosis

2013 ◽  
Vol 228 (6) ◽  
pp. 1048-1062 ◽  
Author(s):  
Chengliang Li ◽  
Zhongsheng Wang ◽  
Shuhui Bu ◽  
Hongkai Jiang ◽  
Zhenbao Liu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Extend Kalman Filter ◽  
Combining Data ◽  
Novel Method ◽  
Strong Tracking Filter ◽  
Tracking Filter

A reliable prediction method is very important to avoid a catastrophic failure. This paper presents a novel method for machinery condition prognosis, named least squares support vector regression strong tracking particle filter which is based on least squares support vector regression combing with strong tracking particle filter. There are two main contributions in our work: first, the regression function of least squares support vector regression is extended, which constructs a bridge for the application of combining data-driven method with a recursive filter based on extend Kalman filter; second, an extend Kalman filter-based particle filter is studied by introducing a strong tracking filter into a particle filter. The strong tracking filter is used to update particles and produce importance densities which can improve the performance of the particle filter in tracking saltatory states, and finally strong tracking particle filter improves the prediction performance of least squares support vector regression in predicting saltatory states. In the experiment, it can be concluded that the proposed method is better than classical condition predictors in machinery condition prognosis.

scholarly journals Direct Surge Margin Control for Aeroengines Based on Improved SVR Machine and LQR Method

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Haibo Zhang ◽  
Fengyong Sun
Keyword(s):  
Support Vector Regression ◽  
Stability Margin ◽  
Linear Quadratic Regulator ◽  
Least Square ◽  
Support Vector ◽  
Linear Quadratic ◽  
Inlet Distortion ◽  
Surge Margin ◽  
Engine Stability ◽  
Margin Control

A novel scheme of high stability engine control (HISTEC) on the basis of an improved linear quadratic regulator (ILQR), called direct surge margin control, is derived for super-maneuver flights. Direct surge margin control, which is different from conventional control scheme, puts surge margin into the engine closed-loop system and takes surge margin as controlled variable directly. In this way, direct surge margin control can exploit potential performance of engine more effectively with a decrease of engine stability margin which usually happened in super-maneuver flights. For conquering the difficulty that aeroengine surge margin is undetectable, an approach based on improved support vector regression (SVR) machine is proposed to construct a surge margin prediction model. The surge margin modeling contains two parts: a baseline model under no inlet distortion states and the calculation for surge margin loss under supermaneuvering flight conditions. The previous one is developed using neural network method, the inputs of which are selected by a weighted feature selection algorithm. Considering the hysteresis between pilot input and angle of attack output, an online scrolling window least square support vector regression (LSSVR) method is employed to firstly estimate inlet distortion index and further compute surge margin loss via some empirical look-up tables.

scholarly journals American option pricing with machine learning: An extension of the Longstaff-Schwartz method

2021 ◽  
Vol 19 (3) ◽  
pp. 85-109
Author(s):  
Jingying Lin ◽  
Caio Almeida
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Practical Importance ◽  
Simulated Data ◽  
Basis Functions ◽  
Support Vector ◽  
American Option Pricing ◽  
Machine Learning Methods ◽  
Classification And Regression ◽  
Index Option

Pricing American options accurately is of great theoretical and practical importance. We propose using machine learning methods, including support vector regression and classification and regression trees. These more advanced techniques extend the traditional Longstaff-Schwartz approach, replacing the OLS regression step in the Monte Carlo simulation. We apply our approach to both simulated data and market data from the S&P 500 Index option market in 2019. Our results suggest that support vector regression can be an alternative to the existing OLS-based pricing method, requiring fewer simulations and reducing the vulnerability to misspecification of basis functions.

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