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发酵过程混合神经网络模型及其仿真
引用本文:隋青美,王正欧.发酵过程混合神经网络模型及其仿真[J].系统仿真学报,2002,14(4):415-417.
作者姓名:隋青美  王正欧
作者单位:天津大学系统工程研究所,天津,300072
摘    要:提出了一种新型的发酵过程混合神经网络模型,该模型由非线性神经网络和线性神经网络两部分组成,由于非线性神经网络采用结构具有线形式的Flat网络,两个网络能够合并为同一表达式,并具有线性形式,可采用线性最小二乘法求解网络权值,与串联结构及串并联结构混合神经网络模型相比,该模型训练方式简单,并可方便地使用在线辨识算法。

关 键 词:发酵过程  混合神经网络  模型  仿真  状态估计  非线性规划
文章编号:1004-731X(2002)04-0415-03
修稿时间:2001年4月11日

A Hybrid Neural Network Model and Simulation for Fermentation Processes
SUI Qing-mei,WANG Zheng-ou.A Hybrid Neural Network Model and Simulation for Fermentation Processes[J].Journal of System Simulation,2002,14(4):415-417.
Authors:SUI Qing-mei  WANG Zheng-ou
Abstract:A new hybrid neural network model for fermentation processes is proposed, which combines the nonlinear network and the linear network. The nonlinear neural network is the Flat network that can be formulated as linear form. The nonlinear network is merged into a linear formula with the linear network. Thus, this formulation makes it easy to update the weight values of the network using a linear least-square method. Compared to existing models containing serial and serial-parallel hybrid neural network approaches in which more costly training is needed, the proposed model is very attractive if accuracy and easy training are critical issues.
Keywords:fermentation processes  neural networks  model building  state estimation  
本文献已被 CNKI 维普 万方数据 等数据库收录!
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