| 引用本文: | 廖迎新,吴敏.基于免疫FNN算法的加热炉炉温优化控制.[J].国防科技大学学报,2006,28(3):116-119.[点击复制] |
| LIAO Yingxin,WU Min.The Reheating Furnace Temperature Optimization Control Based on Immune and Fuzzy Neural Network[J].Journal of National University of Defense Technology,2006,28(3):116-119[点击复制] |
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| 基于免疫FNN算法的加热炉炉温优化控制 |
| 廖迎新1,2, 吴敏1 |
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(1.中南大学 信息科学与工程学院，湖南 长沙 410083;2.中南林业大学 电子与信息工程学院，湖南 长沙 410004)
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| 摘要: |
| 针对复杂钢坯加热过程，提出了一种免疫克隆进化模糊神经网络(ICE-FNN)控制算法。首先根据现场样本数据建立过程神经网络模型；然后基于该模型，采用模糊神经网络控制器(FNNC)规则优化算法，确定FNNC的最佳规则数；最后由FNNC的规则优化所得参数构造初始种群的一个解，采用免疫克隆进化(ICE)算法对FNNC参数优化。该算法具有全局寻优和局部求精能力，仿真结果证实了其有效性。 |
| 关键词: 模糊神经网络 规则优化 免疫克隆进化 加热炉 |
| DOI： |
| 投稿日期：2005-12-15 |
| 基金项目:国家杰出青年科学基金项目(60425310)；湖南省科技计划项目(04FJ3029) |
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| The Reheating Furnace Temperature Optimization Control Based on Immune and Fuzzy Neural Network |
| LIAO Yingxin1,2, WU Min1 |
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(1.College of Information Science and Engineering, Central South Univ., Changsha 410083, China;2.2.College of Electron and Information Engineering, Central South Forestry Univ., Changsha 410004, China)
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| Abstract: |
| The immune clone evolutionary (ICE) algorithm is presented to optimize the parameters of the fuzzy neural network (FNN) controller for the complex billet heating process. First, the neural model of the process was set up via the observation data; then, based on the model, the rule optimization algorithm of the fuzzy neural network controller (FNNC) was introduced to obtain the optimal rule numbers of the FNNC. Finally, by combining the FNNC parameters obtained with the rule optimization into an individual in the initial population, the ICE algorithm was adopted to optimize the parameters of the FNNC. The proposed search strategy is a global optimization and local precision optimization algorithm. Results from simulations prove the effectiveness of the constructed system. |
| Keywords: fuzzy neural network(FNN) rule optimization immune clone evolutionary reheating furnace |
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