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Extracting Fuzzy Rules Based On Fusion Of Soft Computing In Oil Exploration Management

机译：石油勘探管理中基于软计算融合的模糊规则提取

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摘要

This paper proposed a self-learning,self-adapting algorithm (ANN-GA-Cascades) for extracting fuzzy rules,which is based on fusion of soft computing.We could use it to attain the fuzzy rules of oilincss in oil exploration: firstly,supervised learning of training sample is performed by using neural networks,with the inputs being the simplest well-logging attribute set which is relevant to the oiliness attributes,and the outputs being the corresponding oiliness partition C_k (dry layer,water layer,inferiority layer and oil layer).When the neural network attained precision or the maximum iteration steps,the Ath output node of neural network will be the corresponding partition of decision character,with the output function being ψ_k=f(X_i,(WGI)_(ij),(WG2)_(jk)),in which (WGI)_(ij) are the connection weights between input layer and dden layer,(WG2)_(jk) are the connection weights between hidden layer and output layer.Then,the genetic algorithm (GA) was used to randomly assemble the input character and ψ_k as the fitness function.In this way,the optimal chromosome will be the fuzzy rule of partition C_k.Finally,the empirical study application of this algorithm on oil well oilsk81 and oilsk83 of Jianghan oilfield in China has proved to be satisfactory.
机译：本文提出了一种基于软计算融合的自学习，自适应算法（ANN-GA-Cascades），用于提取模糊规则。在石油勘探中，可以用它来获得含油量的模糊规则：使用神经网络进行训练样本的监督学习，输入是与油性相关的最简单的测井属性集，输出是对应的油性分区C_k（干层，水层，劣等层和油层）。当神经网络达到精度或最大迭代步长时，神经网络的Ath输出节点将成为决策字符的对应分区，输出函数为ψ_k= f（X_i，（WGI）_（ij） （WG2）_（jk）），其中（WGI）_（ij）是输入层与dden层之间的连接权重，（WG2）_（jk）是隐藏层与输出层之间的连接权重。遗传算法（GA）用于随机组装输入字符这样，最优染色体将成为分区C_k的模糊规则。最后，该算法在中国江汉油田油井oilsk81和oilsk83的经验研究应用被证明是令人满意的。

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