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中文摘要：
针对传统神经网络进行抽油机示功图识别诊断时受同步瞬时输入限制,不能有效体现连续输入信号的时间累积效应,诊断精度偏低的问题,提出一种极限学习离散过程元网络,模型内部通过三次样条数值积分处理离散样本和权值的时域的聚合运算.模型训练算法采用极限学习,将模型训练转化为最小二乘问题,通过利用Moore-Penrose广义逆和隐层输出权值矩阵来计算输出权值,提升模型学习速度.进行示功图识别时,直接将位移和载荷离散时间序列作为模型输入,对常见的5种示功图状态进行识别.实验结果表明,该方法具有较高的识别精度,同时相对于其它过程神经网络模型,学习速度较快.
英文摘要：
When a pumping well indicator diagram is diagnosed by traditional artificial neural networks,the model is limited by the synchronous instantaneous input. It cannot reflect the cumulative time effect for continuous input signal and has low diagnostic accuracy. Aiming at solving this problem,we propose an extreme learning discrete process neural network. Three-spline numerical integration is applied to deal with the aggregation of discrete samples and weights in the time-domain. An extreme leaning algorithm is applied to the model＇ s training and converts it to a least squares problem. The Moore-Penrose generalized inverse matrix and a hidden layer output matrix are used to compute the output weight. The training speed is enhanced. When the model is used to diagnose five common statuses in the indicator diagram,the discrete time sequence data on the displacement and load are taken as the model input. The experimental results show that the method has higher identification accuracy and faster learning speed than other process neural network.
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