CAJ | 학술논문

工业控制场合中,需要获取非线性被控对象的结构特性,而系统动态响应的数据直接从外部特征上反映了非线性系统结构关系.为了充分利用非线性动态系统响应过程中的数据,本文提出了一种基于滑动数据窗口 (sliding data window)的贝叶斯-高斯神经网络 (SW-BGNN)模型.该模型将数据融合于网络模型结构中,借助于贝叶斯推理和高斯假设,利用滑动窗口数据,实现非线性动态系统的辨识和预测.整个SW-BGNN本身需要确定的参数很少,因此运算的时间很短,适合于非线性动态系统的在线辨识.将SW-BGNN应用于几个非线性动态系统的辨识和预测,仿真试验结果表明了SW-BGNN模型的有效性.
공업공제장합중,수요획취비선성피공대상적결구특성,이계통동태향응적수거직접종외부특정상반영료비선성계통결구관계.위료충분이용비선성동태계통향응과정중적수거,본문제출료일충기우활동수거창구 (sliding data window)적패협사-고사신경망락 (SW-BGNN)모형.해모형장수거융합우망락모형결구중,차조우패협사추리화고사가설,이용활동창구수거,실현비선성동태계통적변식화예측.정개SW-BGNN본신수요학정적삼수흔소,인차운산적시간흔단,괄합우비선성동태계통적재선변식.장SW-BGNN응용우궤개비선성동태계통적변식화예측,방진시험결과표명료SW-BGNN모형적유효성.
In industrial control, the structure of the nonlinear dynamic system is determined by using the dynamic data of the controlled object. In order to make full use of the data obtained from the dynamic response process of the nonlinear dynamic system, a novel Bayesian-Gaussian neural network based on sliding-window(SW-BGNN) is proposed which combines the Bayesian reasoning formula with the Gaussian assumption. Based on the data in the sliding window,the operation process of SW-BGNN reasonably predicts the output of the nonlinear dynamic system in terms of a small number of parameters of the SW-BGNN. The SW-BGNN has limited computation time which makes it suitable to onlinear identification applications. Examples of identification and prediction of nonlinear dynamic system are presented.Simulation results show the effectiveness of the SW-BGNN method.