| 基于递归补偿模糊神经网络的发酵过程建模 |
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| 引用本文： | 潘丰,李海波,顾蕊.基于递归补偿模糊神经网络的发酵过程建模[J].南京理工大学学报(自然科学版),2005,29(Z1):108-111. |
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| 作者姓名： | 潘丰 李海波 顾蕊 |
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| 作者单位： | 江南大学,控制科学与工程研究中心,江苏,无锡,214122 |
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| 摘 要： | 提出一种新型的动态网络———递归补偿模糊神经网络,结合弹性BP算法,把它应用于某多粘菌素的发酵过程的建模与状态预估。仿真结果表明该网络模型训练步数少,训练误差小,收敛速度较快,能够较准确地拟合过程的动态特性,预估精度较高,可用于发酵过程的优化控制。
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| 关 键 词： | 递归补偿模糊神经网络 建模 弹性BP算法 发酵 |
| 文章编号： | 1005-9830(2005)S0-0108-04 |
| 修稿时间： | 2005年5月30日 |
Fermentation Process Modeling Based on Recurrent Compensatory Neuro-fuzzy Network |
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| PAN Feng,LI Hai-bo,GU Rui.Fermentation Process Modeling Based on Recurrent Compensatory Neuro-fuzzy Network[J].Journal of Nanjing University of Science and Technology(Nature Science),2005,29(Z1):108-111. |
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| Authors: | PAN Feng LI Hai-bo GU Rui |
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| Abstract: | A new dynamic neural network-recurrent compensatory neuro-fuzzy network is proposed.Combined with resilient BP algorithm,it is applied to the model and predicts the variables of a mycetozoan fed-batch fermentation process.The result shows that the neural network model is of less training steps,small training error and rapid convergence speed, and can approximate the dynamic characteristic of the process perfectly,and that the predictive precision is higher.So it is helpful to apply in the optimization control of the fermentation process. |
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| Keywords: | recurrent compensatory fuzzy-neuro network modeling resilient BP algorithm fermentation |
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