An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models

2002 ◽  
Vol 14 (11) ◽  
pp. 2647-2692 ◽  
Author(s):  
Harri Valpola ◽  
Juha Karhunen
Keyword(s):  
State Space ◽  
Ensemble Learning ◽  
Latent Variable ◽  
State Space Model ◽  
Dynamic State ◽  
Learning Method ◽  
Nonlinear Prediction ◽  
Higher Dimensional ◽  
Prediction Techniques ◽  
Multilayer Perceptron Networks

A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear mapping from unknown factors. The dynamics of the factors are modeled using a nonlinear state-space model. The nonlinear mappings in the model are represented using multilayer perceptron networks. The proposed method is computationally demanding, but it allows the use of higher-dimensional nonlinear latent variable models than other existing approaches. Experiments with chaotic data show that the new method is able to blindly estimate the factors and the dynamic process that generated the data. It clearly outperforms currently available nonlinear prediction techniques in this very difficult test problem.

A New Dynamic State Space Model for the Cylindrical Grinding Process

2005 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Surface Roughness ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Grinding Process ◽  
Cylindrical Grinding ◽  
Space Model ◽  
Proposed Model ◽  
Grinding Power ◽  
Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

A Dynamic State Space Model of a MHO Distance Relay

1985 ◽  
Vol PAS-104 (12) ◽  
pp. 3558-3564 ◽  
Author(s):  
Z. Peng ◽  
M. Li ◽  
C. Wu ◽  
T. Cheng ◽  
T. Ning
Keyword(s):  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Distance Relay ◽  
Space Model

A Dynamic State Space Model of an MHO Distance Relay

1985 ◽  
Vol PER-5 (12) ◽  
pp. 51-52
Author(s):  
Z. Peng ◽  
M. S. Li ◽  
C. Y. Wu ◽  
T. C. Cheng ◽  
T. S. Ning
Keyword(s):  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Distance Relay ◽  
Space Model

scholarly journals Wireless Charging Efficiency Optimization Method Based on Adaptive Robust Control

2021 ◽  
pp. 332-340
Author(s):  
Junping Qi , Yanhua Lei ,Pengxiang Qi
Keyword(s):  
State Space ◽  
Power Transmission ◽  
State Space Model ◽  
Adaptive Robust Control ◽  
Dynamic State ◽  
Wireless Charging ◽  
Characteristic Analysis ◽  
Space Model ◽  
Charging System ◽  
Voltage Dependent

This paper designs, analyzes and optimizes the electric vehicle wireless charging system and its control method. Compared with the traditional plug-in conduction charging, wireless charging is more convenient, safe, reliable and has better environmental adaptability. Inductive power transmission technology (IPT) is the main technology for wireless charging of electric vehicles and plug-in hybrid vehicles. Based on the fundamental approximation method, the critical self inductance of the primary or secondary coil is derived in this paper. This critical value reflects the power transmission capacity and control performance of the two chargers when they are interoperable. Compared with the non integrated structure, the magnetically integrated LCC compensated wireless charging system can transmit the same power with smaller compensation inductance. Based on the voltage dependent dynamic state space model, four working modes of magnetically integrated LCC compensated wireless charging system are studied in this paper. Simulation and experimental results show that the voltage dependent equivalent circuit model can be effectively applied to the basic characteristic analysis of wireless charging system. The voltage dependent dynamic state space model is more accurate and realistic. In addition to reflecting the basic characteristics of the wireless charging system, it can also reflect the working mode of the wireless charging system.

scholarly journals Nonlinear Offset-Free Model Predictive Control Based on Dynamic PLS Framework

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1784
Author(s):  
Qiang Zhao ◽  
Xin Jin ◽  
Huapeng Yu ◽  
Shan Lu
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Latent Variable ◽  
State Space Model ◽  
Fuzzy Model ◽  
The State ◽  
Partial Least Square ◽  
Space Model ◽  
Free Model

A nonlinear offset-free model predictive control based on a dynamic partial least square (PLS) framework is proposed in this paper. A multi-output multi-input system is projected into latent variable space by a PLS outer model. For each latent variable model, the T–S fuzzy model is used to describe the nonlinear characteristics of the system; while the state-space model is used in T–S fuzzy model consequent parameters to describe the dynamic characteristics. A disturbance model is introduced in the state-space model. For model state variables, a state observer is used to compensate for the mismatch of the model. The case study results for the pH neutralization process show that the MPC controller based on this method can guarantee the tracking performance of the nonlinear system without static error.

scholarly journals On Sequential Learning for Parameter Estimation in Particle Algorithms for State-Space Models

2016 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Chunlin Ji
Keyword(s):  
State Space ◽  
Blind Deconvolution ◽  
State Space Model ◽  
The State ◽  
State Space Models ◽  
Sequential Learning ◽  
Model Parameters ◽  
Learning Method ◽  
Space Model ◽  
Deconvolution Problem

Particle methods, also known as Sequential Monte Carlo, have been ubiquitous for Bayesian inference for state-space models, particulary when dealing with nonlinear non-Gaussian scenarios. However, in many practical situations, the state-space model contains unknown model parameters that need to be estimated simultaneously with the state. In this paper, We discuss a sequential analysis for combined parameter and state estimation. An online learning method is proposed to approach the distribution of the model parameter by tuning a flexible proposal mixture distribution to minimize their Kullback-Leibler divergence. We derive the sequential learning method by using a truncated Dirichlet processes normal mixture and present a general algorithm under a framework of the auxiliary particle filtering. The proposed algorithm is verified in a blind deconvolution problem, which is a typical state-space model with unknown model parameters. Furthermore, in a more challenging application that we call meta-modulation, which is a more complex blind deconvolution problem with sophisticated system evolution equations, the proposed method performs satisfactorily and achieves an exciting result for high efficiency communication.

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