摘要
神经元网络应用于一般工业过程建模比较有效,但对于对象是庞大数据的复杂的工业生产过程就明显力不从心,提出了基于势场拓扑的层次聚类算法和模糊c均值聚类算法融合,以获得精确的聚类个数和隶属度,基于多判据信息融合和模糊技术构建神经元网络模型,通过对实际地下开采生产过程的仿真,结果验证了模型的有效性。
Apply the neural network in the general industry model is effective. However,meural network' s strength does not match the huge data complex industrial production process' s ambitions. Propose fusion algorithm that based on the topology of the Potential Field algorithm and fuzzy c mean algorithm, obtain the precise clustered number and the membership degree. Construct the neural network model based on multi-criterion information fusion and the fuzzy technology, through the actual simulation on coal mining production procesas, the result confirms that the model is valid.
出处
《河北理工大学学报(自然科学版)》
CAS
2008年第3期70-73,82,共5页
Journal of Hebei Polytechnic University:Social Science Edition
基金
国家自然科学资助项目"煤矿井下电磁兼容性基础研究"(50674093)
关键词
层次聚类
模糊C均值聚类
模糊神经元网络
信息融合
the potential field algorithm
Fuzzy cmean algorithm
Fuzzy neural network information fusion