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A ls-svm-based Internet real-time signal transmission method

A LS-SVM model and real-time signal technology, applied in the transmission system, digital transmission system, data exchange network, etc., to achieve the effect of improving prediction accuracy, reducing occupancy rate, and reducing the amount of network transmission data

Inactive Publication Date: 2016-08-17
JILIN UNIV
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Problems solved by technology

[0017] The present invention provides an LS-SVM-based Internet real-time signal transmission method to solve the problem of prediction and transmission of nonlinear signals in the Internet

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  • A ls-svm-based Internet real-time signal transmission method
  • A ls-svm-based Internet real-time signal transmission method
  • A ls-svm-based Internet real-time signal transmission method

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Embodiment Construction

[0040] Under the DPS framework, the LS-SVM model is used to predict the signal. Such as figure 1 shown. Physical sensors periodically measure and sample real physical phenomena, and send uniform sampling signals to the LS-SVM prediction model; in the LS-SVM prediction model, use training samples to learn parameters and kernel functions to determine the appropriate Model parameters. In this part, the conversion of sampling data to prediction model is completed; the model parameters are sent to the Internet in the form of data frames through the data sending module; the receiving module receives the data frames and analyzes them to obtain the prediction model parameter information, and sends The information is sent to the LS-SVM prediction model; LS-SVM updates the prediction model and estimates the sampled data.

[0041] The symbols involved in the present invention are explained as follows:

[0042] ε s The prediction error threshold defined by the sending end and the appl...

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Abstract

The invention relates to an LS-SVM-based Internet real-time signal transmission method, which belongs to the intersection of the two technical fields of computer network and signal processing. The steps include: an initialization phase, an interaction process of model information, a model update process at the sending end, and a signal reconstruction process at the application end. The advantages are: the LS‑SVM-based DPS double prediction model created at the same time at the sender and the application side adopts the DPS mechanism, and within the expected error range, the transmission of the prediction model is used to replace the transmission of the sampled data, which greatly reduces the network transmission data. It can effectively reduce the occupancy rate of network bandwidth. Use LS‑SVM online prediction to improve the prediction accuracy of nonlinear signals in the Internet, and solve the reliability problems of traditional DPS methods in nonlinear signal network transmission; greatly reduce the training time of the model, only need to calculate the updated Lager Rangian multipliers and bias values.

Description

technical field [0001] The present invention belongs to the intersection of the two technical fields of computer network and signal processing, especially relates to solving the problem of real-time signal transmission in the network based on the LS-SVM online double prediction mechanism (DPS, Dual Prediction Scheme), and alleviates the problem by means of prediction. Data loss and delay caused by network transmission affect real-time signals. Background technique [0002] With the integration and convergence of several technologies such as sensors, microelectronics, embedded computing and communications, micro sensors with the ability to perceive information, data processing, storage and communication are used in many fields such as national defense, military, industrial production, and environmental monitoring. . Wireless sensor nodes cooperatively perceive, collect and process a large number of real-time signals in the geographical area covered by the network, and transm...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24H04L12/26
Inventor 董劲男秦贵和陈虹孙丹郑啸天任鹏飞张超杰王雪秦俊陆帅冰孙丽娜郭悦
Owner JILIN UNIV
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