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State estimation method of nonlinear system

A nonlinear system and state estimation technology, applied in the field of state estimation of nonlinear systems, can solve the problems of limited accuracy of unscented Kalman filtering and reduced estimation performance, and achieve the effect of self-adaptive adjustment and improvement of estimation accuracy.

Inactive Publication Date: 2017-08-15
QUZHOU UNIV
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Problems solved by technology

However, the accuracy of the unscented Kalman filter is still limited, and when the system dimension is high, its estimation performance is significantly reduced

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  • State estimation method of nonlinear system
  • State estimation method of nonlinear system
  • State estimation method of nonlinear system

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[0063] The implementation of the present application will be described in detail below with reference to the accompanying drawings and examples, so as to fully understand and implement the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects.

[0064] State Space Models of Neural Networks

[0065] The structural models of neural networks can generally be divided into feedforward neural network models, feedback neural network models, and stochastic neural network models. At present, the feedforward neural network model is the most widely used in various industries. The model structure of its state space is as follows: figure 1 shown, where x 1 ,x 2 ,...x n The sample node used to represent the input, y 1 ,y 2 ,...y m Used to represent the output sample node, θ 1 ,θ 2 ,...θ q Represents the weight coefficient. The neural network model structure has three node layers, namely the input layer, hi...

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Abstract

The invention discloses a high-order volume Kalman filtering and neural network-based state estimation method. The method is realized through the following steps of: firstly establishing a state space model for a nonlinear system by utilizing a neural network; combining a weight of the neural network and a state variable of the system to serve as a new state variable; and updating the new state in real time by adoption of high-order volume Kalman filtering, so as to achieve the real approach, to a nonlinear system model, of the neural network and the accurate estimation of a state value. Through experimental simulation, the effectiveness of the method is proved.

Description

technical field [0001] The application belongs to the technical fields of aerospace, intelligent transportation, pattern recognition and medical engineering, and in particular relates to a state estimation method of a nonlinear system. Background technique [0002] When the nonlinear system state model is unknown, the existing algorithms based on nonlinear filtering and neural network are limited in the accuracy of nonlinear system state estimation. [0003] When the system model is unknown, it is a simple and effective method to use neural network to approximate the model nonlinearly. The neural network determines the nonlinear function approximated by the network according to the actual data set. However, when the state variables of the actual system are not completely measurable, the neural network alone will not be able to establish a model of the process. The state space model method describes the change relationship between the internal state of the system and the ext...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/11
CPCG06F17/11
Inventor 许大星王海伦张露柴国飞陈佳泉
Owner QUZHOU UNIV
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