基于模糊神经元网络的信息融合模型

为了解决对象是庞大数据的复杂的工业生产过程的建立模型困难的问题,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,结果表明:通过两次聚类算法的融合,获得精确的聚类个数和隶属度,以此来确定模糊神经网络的结构模型,并将得到的隶属度作为依据,建立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。该成果对煤矿开采具有一定的参考价值和指导意义。

Research based on neural Network information fusion model

In order to-solve the problem that is difficult to establish the model of the huge data complex industrial production process.Propose fusion algorithm that based on the topology of the Potential Field algorithm and fuzzy c mean algorithm,the result indicate:through fuse twice cluster algorithm,obtain the precise clustered number and the,determine the fuzzy neural network's structure,and according to that 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 process,the result confirms that the model is valid.This achievement has certain reference value and the guiding sense to the coal mining.