共查询到17条相似文献,搜索用时 156 毫秒
1.
在模糊ART神经网络的基础上,有机结合模糊模式识别和模糊聚类算法,并通过引入新的学习机制和优化网络结构,建立了改进的新型模糊ART神经网络模型;同时,结合某钢厂连铸现场采集的历史数据,将该模型应用于连铸漏钢预报过程中。其结果表明,该模型对粘结漏钢过程中2种典型温度模式的预报率分别达到956%和978%,报出率都达到100%,且在避免漏报的同时保证了较低的误报率,能准确识别典型的温度模式和预测拉漏事故的发生。 相似文献
2.
3.
4.
5.
连铸是炼钢过程非常重要的工序之一。连铸过程多发漏钢事故,其影响因素多且机理复杂,其中以黏结漏钢最为常见,约占总漏钢事故的70%。连铸漏钢事故造成钢液泄漏,容易发生灼烫、火灾甚至爆炸等安全事故,造成人员伤亡和巨大的财产损失。为解决上述问题,剖析了黏结裂口的扩展方式和黏结漏钢的形成机理;基于热电偶测温法预报黏结漏钢的原理,利用神经网络建立黏结漏钢预报模型,并运用遗传算法完成神经网络的优化,预报模型测试样本的正确报出率达到100%,预报率为97.56%;对空间网络模型进行了验证,A型空间网络模型的输出符合期望,可以实现黏结在空间裂口扩展的预报。模型具有很好的应用价值,可为连铸安全生产提供支撑。 相似文献
6.
7.
在河北钢铁集团唐山钢铁集团有限责任公司板坯连铸生产中,浇铸过程中的漏钢是典型的事故。为了尽可能降低连铸过程中漏钢事故所造成的损失,上海宝信软件股份有限公司开发了连铸漏钢预报系统,通过将逻辑判断方法和机器学习方法相结合,大大提高了漏钢预报模型的准确性,相应减少了漏报和误报给连铸生产带来的经济损失。 相似文献
8.
利用模糊控制器和神经元的优点,基于漏钢预报较成熟的热电偶方法,设计出一种基于漏钢预报的连铸拉速模糊控制系统,在保证不产生漏钢的前提下尽可能提高铸坯的拉速,从而提高钢产量。离线模拟结果了该方法的有效性。 相似文献
9.
10.
11.
12.
模糊神经网络在30MnSi金属塑性变形抗力预报中的应用 总被引:2,自引:0,他引:2
采用模糊神经网络,建立轧钢生产过程中30MnSi钢轧制温度,变形程序,变形速率和变形抗力之间的数学模型,预报金属塑性变形抗力,离线预报表明,模糊神经网络预报金属塑性变形抗力有较高的精度。 相似文献
13.
在运用模糊神经网络进行预测的基础上,建立了一种应用小波理论对时间信号进行去噪,根据去噪处理对模糊神经网络作相应处理的预测模型,并将所建模型应用于高炉炉温预测。仿真结果证明小波模糊神经网络比模糊神经网络更具优越性,预测准确率明显提高。 相似文献
14.
15.
16.
Fuzzy Neural Model for Flatness Pattern Recognition 总被引:5,自引:0,他引:5
For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition. 相似文献
17.
In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC (cerebellar model articulation controllers) neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved. 相似文献