Improved kernel extreme learning machine locating method

Abstract

The invention relates to wireless locating. For improving the locating precision, reducing the sample data dimension and increasing the locating speed, a locating prediction model is obtained. The technical scheme adopted by the invention is an improved kernel extreme learning machine locating method. The method comprises the steps of firstly, obtaining training data by adopting a method for performing multi-time measurement in a same position; secondly, dividing the data measured in the same position into a sample sub-space, extracting features of the sample sub-space, and replacing originaltraining data with the features of the sample sub-space; meanwhile, improving a kernel extreme learning machine algorithm by utilizing related theories of matrix approximation and matrix extension; and finally, training the obtained processed training data by utilizing an improved kernel extreme learning machine to obtain the locating prediction model, and performing position estimation by using the obtained locating prediction model to achieve the purpose of locating. The method is mainly applied to the wireless locating occasion.

Description

technical field [0001] The invention relates to the fields of wireless positioning, machine learning and neural network algorithm research, and specifically relates to an improved nuclear extreme learning machine positioning method. Background technique [0002] In recent years, with the development of neural networks becoming more and more mature, neural networks have been widely used in various fields such as manual control, image analysis, and intelligent prediction. Because the neural network has the advantages of strong anti-interference ability, strong nonlinear mapping ability, and strong self-learning ability, many scholars apply the neural network in the field of wireless positioning. For example: RBF neural network, BP neural network, SVM support vector machine, ELM (extreme learning machine) and other neural networks have been applied to wireless positioning. Neural network localization is mainly divided into two parts: training and prediction. In the training p...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies of the prior art, the present invention aims to propose an improved kernel extreme learning machine positioning algorithm for the problems that the current neural network wireless positioning takes a long time and the positioning results are easily disturbed by noise.

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