System and method for online training support vector machine soft-sensing model

Abstract

The invention discloses a dual-core embedded platform-based system for online training a support vector machine soft-sensing model, which mainly comprises a microcontroller, a memory, a human-computer interaction interface, a communication interface, a signal input and output interface and the like. An embedded platform-based method for online training the support vector machine soft-sensing model of the invention can maximally meet the requirements on real-time performance and intelligence because related parameters can be automatically set according to data characteristics and manual parameter setting is simultaneously supported. The system has a plurality of functions of being portable, supporting dynamic display and intelligent analysis and the like, realizes complex operations through the simple and direct-viewing human-computer interaction interface, overcomes the defects of difficulty in the realization of miniaturization, difficulty in the determination of related training parameters, the complex operations and the like, particularly the successful transplantation of the online training of a support vector machine algorithm, and provides a scheme which is low in cost, portability and high in the real-time performance for soft-sensing in complex processes in petrochemical technology and the like.

Description

technical field [0001] The invention relates to an online training system and method for a soft sensor model, in particular, an online training system and method for a support vector machine soft sensor model based on an embedded platform. Background technique [0002] With the development of technology and applications, control systems are becoming more and more complex. For difficult-to-control process problems such as multi-input and multi-output, time-varying, nonlinear, and large time-delay, it is necessary to use intelligent algorithms such as support vector machines and soft sensor technology to achieve ideal results. However, these complex algorithms are mostly based on large-scale systems such as DCS and FCS, and the system cost is relatively high. The operating environment of the algorithm is also mostly based on platforms such as industrial computers, which are difficult to meet the requirements of embedded and portable. [0003] Embedded platforms and devices a...

AI Technical Summary

Problems solved by technology

[0004] Although there are embedded applications of support vector machine intelligent algorithms such as robots and mobile phone handwriting recognition, most of these applications use offline training, and only transplant the trained results to the embedded platform, without transplanting the training or optimization process to the embedded platform. in the platform

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