A Neuro-Fuzzy System Modeling using Gaussian Mixture Model and Clustering Method

The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

Download Full-text

Improved Model for On-Board Real-Time by Constructing Empirical Model via GMM Clustering Method

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20203830507 ◽

2020 ◽

Vol 38 (3) ◽

pp. 507-514

Author(s):

Hao Sun ◽

Yingqing Guo ◽

Wanli Zhao

Keyword(s):

Real Time ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Empirical Model ◽

Gaussian Mixture ◽

Fault Isolation ◽

Adaptive Model ◽

Clustering Method ◽

Time Model ◽

Improved Model

The method of constructing an empirical model is used to compensate the deviation between the output of the on-board real-time model and the engine measurement parameters, and improve the parameter tracking and estimation performance of the on-board adaptive model in the full flight envelope. Due to the large amount of data acquired online, the clustering method based on Gaussian mixture model is implemented to realize data compression for offline training and updating the empirical model. The present empirical model is applied to the on-board adaptive model of civil large bypass ratio turbofan engine. The simulation results show that the empirical model based on Gaussian mixture model can reduce the output error of on-board real-time model, and the accuracy of the health parameter estimation and engine component fault isolation performance of the on-board real-time adaptive model with empirical model are improved.

Download Full-text

A Neuro-Fuzzy Modeling using the Hierarchical Clustering and Gaussian Mixture Model

Journal of Korean institute of intelligent systems ◽

10.5391/jkiis.2003.13.5.512 ◽

2003 ◽

Vol 13 (5) ◽

pp. 512-519 ◽

Cited By ~ 2

Author(s):

Sung-Suk Kim ◽

Keun-Chang Kwak ◽

Jeong-Woong Ryu ◽

Myung-Geun Chun

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Hierarchical Clustering ◽

Gaussian Mixture ◽

Fuzzy Modeling ◽

Neuro Fuzzy

Download Full-text

A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Theory and Scheme

Monthly Weather Review ◽

10.1175/mwr-d-16-0064.1 ◽

2017 ◽

Vol 145 (7) ◽

pp. 2743-2761 ◽

Cited By ~ 3

Author(s):

Tapovan Lolla ◽

Pierre F. J. Lermusiaux

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

System Modeling ◽

Gaussian Mixture ◽

Field Equations ◽

Stochastic Dynamical Systems ◽

Earth System Modeling ◽

Full State ◽

Geophysical Processes ◽

Fundamental Equations

Retrospective inference through Bayesian smoothing is indispensable in geophysics, with crucial applications in ocean and numerical weather estimation, climate dynamics, and Earth system modeling. However, dealing with the high-dimensionality and nonlinearity of geophysical processes remains a major challenge in the development of Bayesian smoothers. Addressing this issue, a novel subspace smoothing methodology for high-dimensional stochastic fields governed by general nonlinear dynamics is obtained. Building on recent Bayesian filters and classic Kalman smoothers, the fundamental equations and forward–backward algorithms of new Gaussian Mixture Model (GMM) smoothers are derived, for both the full state space and dynamic subspace. For the latter, the stochastic Dynamically Orthogonal (DO) field equations and their time-evolving stochastic subspace are employed to predict the prior subspace probabilities. Bayesian inference, both forward and backward in time, is then analytically carried out in the dominant stochastic subspace, after fitting semiparametric GMMs to joint subspace realizations. The theoretical properties, varied forms, and computational costs of the new GMM smoother equations are presented and discussed.

Download Full-text