| 摘要: |
| 对于多输人多输出系统,针对如何根据系统模型和期望输出反求系统输入的问题,提出了一种基于过程神经 网络和量子遗传算法相结合的方法,并给出了具体的实现方法。首先根据实际系统的领域知识和学习样本集,建立 满足系统实际输人输出映射关系的正向过程神经网络；然后按照系统在过程区间的某一期望输出，用过程神经网络 的输出误差构造适应度函数,用量子遗传算法逆向确定系统的过程输人信号，使该输人信号满足巳建立的正向过程 映射关系,从而完成系统的逆向过程控制。油藏采收率参量的逆向求解结果证明了该方法的有效性。 |
| 关键词: 过程神经网络 量子遗传算法 油藏采收率 逆向求解 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金重点项目(50643020) |
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| Converse solution of oil recovery ratio based on process neural network and quantum genetic algorithm |
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XU Zeng-fu1,2,WANG Hong-wei3,WU Gui-sheng1
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(1. School of Economics & Management, Tsinghua University, Beijing 100084, China;2. Daqing Petroleum Institute, Daqing 163318, Heilongjiang Province, China ;3. The Sixth Oil Production Plant，Daqing Oilfield Limited Company, Daqing 163300, Heilongjiang Province, China)
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| Abstract: |
| An optimization algorithm of process neural networks and quantum genetic algorithm ( PNN-QGA) was proposed to ascertain the input of multiple-input and multiple-output (MIMO) system from both system model and hope output. And the general realization approach was presented. Firstly, the process neural network ( PNN) that represents the mapping relation between input and output of system is founded according to system field knowledge and training samples sets. Secondly, the fitness function of quantum genetic algorithm ( QGA) is constructed by using PNN output error based on the hope output of process interval. The system input information is ascertained by QGA according to a certain hope output of system，and it accords with the PNN mapping relation that is founded. Hence，the converse process solution of the system is accomplished. Finally, a converse-solving example of oil recoveiy ratio was given to illustrate the availability of the approach. |
| Key words: process neural network quantum genetic algorithm oil recovery ratio converse solution |
