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ANFIS (Adaptive Neural Fuzzy Inference System) indoor positioning method based on improved GA(Genetic Algorithm) optimization in WLAN (Wireless Local Area Network) environment

An indoor positioning and environmental technology, applied in electrical components, wireless communication, etc., can solve problems such as slow convergence speed of BP algorithm, local minimal genetic algorithm, slow evolution speed, etc., achieve fast global optimization, speed up convergence speed, and simple method Effect

Active Publication Date: 2014-01-08
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide an improved GA-optimized ANFIS indoor positioning method in a WLAN environment in order to solve the problems of slow convergence and easy local minima of the existing BP algorithm and the prematurity and slow evolution of the genetic algorithm

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  • ANFIS (Adaptive Neural Fuzzy Inference System) indoor positioning method based on improved GA(Genetic Algorithm) optimization in WLAN (Wireless Local Area Network) environment
  • ANFIS (Adaptive Neural Fuzzy Inference System) indoor positioning method based on improved GA(Genetic Algorithm) optimization in WLAN (Wireless Local Area Network) environment
  • ANFIS (Adaptive Neural Fuzzy Inference System) indoor positioning method based on improved GA(Genetic Algorithm) optimization in WLAN (Wireless Local Area Network) environment

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specific Embodiment approach 1

[0017] Specific implementation mode one: combine figure 1 Describe this implementation mode, this implementation mode comprises concrete steps as follows:

[0018] Step 1. Arranging several access points AP in the WLAN indoor positioning environment to ensure that any point in the environment is covered by signals sent by two or more access points AP;

[0019] Step 2: Set N reference points in the indoor environment, choose a reference point as the origin to establish a rectangular coordinate system, obtain the coordinate positions of the N reference points in the rectangular coordinate system, and use the signal receiver on each reference point Collect the signal strength value from each access point AP, obtain ANFIS training samples, and establish the corresponding relationship between the actual coordinates of the reference point and the signal strength of the received access point AP, that is, the radio map (Radio Map);

[0020] Step 3. Establish the ANFIS positioning sub...

specific Embodiment approach 2

[0023] Specific implementation mode two: combination figure 2 Illustrate this embodiment, the specific steps of the improved genetic algorithm described in step 4 in the specific embodiment one are:

[0024] Step A: Initialize the population, set the population size, that is, the number of individuals in the population and the maximum genetic algebra; encode the parameters to be adjusted in the ANFIS network with real numbers to form a code string, which is used as the individual gene in the genetic algorithm; ANFIS in each coordinate direction In the positioning subsystem, there are three types of parameters that need to be adjusted: the first type is the rule consequence parameters of the fourth layer of the network, and the second and third types of adjustable parameters are the mean value and standard deviation of the Gaussian membership function in the fuzzy layer; Each parameter to be adjusted corresponds to a bit in the gene code string; each gene code string correspon...

specific Embodiment approach 3

[0043] Specific embodiment three: the implementation method of the BP operator described in step C in the specific embodiment one is: at first obtain training error, then obtain ANFIS network error negative gradient direction correction weight according to BP algorithm, described concrete process is:

[0044] In the L-layer ANFSI network, if there is n on the kth layer k nodes, and there are P groups of input and output data in the training sample set, and the objective function corresponding to the pth (1≤p≤P) group of data is defined as the root mean square error, as follows:

[0045] E = ( 1 p Σ p = 1 P E p ) 1 2 = ...

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Abstract

The invention discloses an ANFIS (Adaptive Neural Fuzzy Inference System) indoor positioning method based on improved GA(Genetic Algorithm) optimization in a WLAN (Wireless Local Area Network) environment, relating to an indoor positioning method in the fields of pattern recognition and artificial intelligence, particularly relating to a WLAN indoor ANFIS positioning method based on the improved GA optimization. The method solves the problems that the BP (Back Propagation) algorithm is slow in convergence rate and easy to trap into the local minimum and the genetic algorithm is premature and slow in evolution speed. The method comprises the steps of: 1, ensuring that anyone point in the environment is covered by signals emitted by two or more access points (AP); 2, building a corresponding relationship between actual coordinates of a reference point and the strength of the received signals of the AP; 3, building an ANFIS positioning sub-system in X direction and Y direction; 4, obtaining a network structure parameter by utilizing the improved ANFIS positioning sub-system; and 5, implementing the positioning of a test point. The ANFIS indoor positioning method based on the improved GA optimization in the WLAN environment is applied to indoor ANFIS positioning in the WLAN.

Description

technical field [0001] The invention relates to an indoor positioning method in the field of pattern recognition and artificial intelligence, in particular to a WLAN indoor ANFIS positioning method optimized based on an improved genetic algorithm. Background technique [0002] With the rapid development of wireless radio networks based on the IEEE802.11 protocol family, many positioning-related technologies and applications have emerged, especially in context-aware applications. Wireless positioning will be the key technology of the next generation mobile communication, and it is also one of the important applications of wireless local area network (WLAN). With the diversity of communication services, wireless positioning has attracted more and more attention, and has important application significance in social public services. [0003] The positioning system suitable for local area networks is called terrestrial wireless positioning technology. Currently, the widely used ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W64/00
Inventor 马琳王嘉胤徐玉滨赵洪林魏守明张成文
Owner HARBIN INST OF TECH
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