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TSK fuzzy model particle filtering method and system for type-2 intuitive fuzzy decision

A fuzzy model and intuitive fuzzy technology, applied in the field of particle filtering, can solve problems such as high redundancy, high particle degradation, and reduced modeling performance, and achieve the effect of reducing the number of rules, reducing degradation problems, and improving accuracy

Active Publication Date: 2020-04-17
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because irrelevant information is cached in the whole of the feature, these irrelevant features may reduce the performance of modeling using all features. In particle filtering, due to the high number of rules and the high redundancy of the model, it causes The degree of particle degradation is higher

Method used

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  • TSK fuzzy model particle filtering method and system for type-2 intuitive fuzzy decision
  • TSK fuzzy model particle filtering method and system for type-2 intuitive fuzzy decision
  • TSK fuzzy model particle filtering method and system for type-2 intuitive fuzzy decision

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Embodiment 1

[0028] The embodiment of the present invention provides a TSK fuzzy model particle filtering method for type-2 intuitionistic fuzzy decision-making, such as figure 1 shown, including the following steps:

[0029] Step S1: Using the type-2 intuitionistic fuzzy decision objective function, select the optimal feature set that can reflect the characteristics of the moving target.

[0030] In practice, the degree of membership of the characteristic attributes of the moving target is a problem that needs to be solved. The decision-making method of evidence theory is used to select the characteristic attribute that can best reflect the mobility of the target. There is no requirement for consistency in feature type and measurement size, and there is no need to do any preprocessing on the series combined feature vector. The fuzzy distribution function is used to process each feature separately, and the combined feature formed by the simple combination of various different features can ...

Embodiment 2

[0088] An embodiment of the present invention provides a type-2 intuitionistic fuzzy decision-making TSK fuzzy model particle filter system, such as Image 6 shown, including:

[0089] The optimal feature set acquisition module 1 is used to use the type-2 intuitionistic fuzzy decision-making objective function to select the optimal feature set that can reflect the characteristics of the moving target; this module executes the method described in step S1 in Embodiment 1, and in This will not be repeated here.

[0090] TSK fuzzy model construction module 2, used to construct the TSK fuzzy model, using the optimal feature set as input, calculate the weight of each rule based on the antecedent membership function of the TSK fuzzy model, and use the method of weighted sum to obtain multiple rules The state fusion result of the fuzzy rules is taken as the output of the TSK fuzzy model; this module executes the method described in step S2 in Embodiment 1, which will not be repeated ...

Embodiment 3

[0094] An embodiment of the present invention provides a terminal, such as Figure 7 As shown, it includes: at least one processor 401 , such as a CPU (Central Processing Unit, central processing unit), at least one communication interface 403 , memory 404 , and at least one communication bus 402 . Wherein, the communication bus 402 is used to realize connection and communication between these components. Wherein, the communication interface 403 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 403 may also include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Ramdom Access Memory, volatile random access memory), or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory 404 may also be at least one storage device located away from the aforementioned processor 401 . The processor 401 can execute the type-2 intuitionistic...

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Abstract

The invention discloses a TSK fuzzy model particle filtering method and system for type-2 intuitionistic fuzzy decision, and the method comprises the steps: selecting an optimal feature set which canreflect the characteristics of a moving target through a type-2 intuitionistic fuzzy decision target function; constructing a TSK fuzzy model, taking the optimal feature set as input, calculating theweight of each rule based on a antecedent membership function of the TSK fuzzy model, and obtaining a state fusion result of a plurality of fuzzy rules by utilizing a weighted summation method to serve as the output of the TSK fuzzy model; and constructing an important density function of particle filtering based on a state fusion result, and extracting particles from the important density function for filtering updating. According to the method, a type-2 intuitionistic fuzzy decision objective function is utilized to select an optimal feature set capable of reflecting the characteristics of amoving object to construct a TSK fuzzy model; the number of rules of the model and the redundancy of the model are reduced, the accuracy of the model is improved, an output result of constructing theTSK fuzzy model is used as an important density function of a particle filtering algorithm, and the degradation problem of particles is greatly reduced.

Description

technical field [0001] The invention relates to the field of particle filtering, in particular to a TSK fuzzy model particle filtering method and system for type-2 intuitionistic fuzzy decision-making. Background technique [0002] Modeling techniques are widely used in many fields to accurately model nonlinear systems. Fuzzy systems have a good ability to describe the complex dynamics of nonlinear behaviors of dynamic processes. Fuzzy model identification is an effective tool for high-precision modeling of complex nonlinear systems based on measured data. Using the nonlinear mapping ability of fuzzy logic, complex nonlinear systems defined on compact sets can be uniformly approximated to arbitrary precision. In the modeling process of TSK fuzzy model, structure identification and parameter identification are very critical steps, which determine the quality of the model. The related work of structure identification includes the determination of the number of rules, the se...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15G06F17/16G06N7/02
CPCG06F17/15G06F17/16G06N7/02G06T2207/20024
Inventor 李良群王小梨谢维信
Owner SHENZHEN UNIV
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