In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the model...In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.展开更多
文摘In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.
文摘针对目前铁路现场对轨道电路故障的判别仍然采用阈值法导致维护效率偏低的问题,提出将神经网络与模糊逻辑相结合,构建区间二型神经模糊系统(interval type-2 neural-fuzzy system,IT2NFS),通过诊断模型实现对轨道电路故障模式的智能识别.首先通过结构识别建立初步的网络结构,采用均匀设计方法生成模糊集的均值,对训练样本进行相似性测试,生成标准差和初始后件参数;再通过递归奇异值分解,优化后件参数以减小输出误差;最后,针对常见的8种故障,从实验平台上采集样本共计9000个,其中6300个样本用于模型训练,剩余2700个用于实验测试.实验结果表明:利用IT2NFS模型进行故障诊断时,每种故障类别的识别率均在82%以上,平均正确率为90.9%,仿真用时10.59 s.
