摘要
控制学术权威Austrom指出:模糊逻辑控制、神经网络和专家系统是三种典型的智能控制方法。由于专家系统在实际应用中有较多的问题和困难(如:知识的获取主要靠人工移植,因此成本高、效率低;采用常规的分明集,致使它的推理能力弱,容易产生匹配冲突、组合爆炸等问题),现在智能控制的重点则集中在模糊逻辑、神经网络以及二者的结合应用上。特别是二者的结合,由于融合了各自的优点,并能在一定程度上克服二者一些固有的缺陷,因而已受到人们的广泛关注,成为智能控制研究的热点。
本文针对单纯模糊系统在传统建模方法上的缺陷:难以提取和调整模糊规则与隶属函数,并且其工作量往往随变量数目的增长呈指数级增长,在前人提出的正规化模糊神经网络(NFNN)结构基础上,改进了已有的规则合并的算法。利用这种算法,可以将由神经网络自动获取的模糊规则进行合并与调整,并通过一个函数的仿真实验验证了算法的有效性。
转炉炼钢过程是一个复杂的多元多相高温反应过程,其模型的建立则是一个典型的多元非线性的映射过程,尤其在大多中小型转炉不宜增设副枪的条件下,如何在传统的静态模型之外寻找性能更优越的控制模型,就显得比较突出和迫切。本文即是把NFNN应用于某钢厂转炉提钒炼钢静态控制模型的建立问题之中,建立了转炉炼钢的冷却剂加料子模型。论文最后的测试结果说明了这种建模方式的有效性与实用性,也再次验证了规则合并的有效性。
Professor Austrom points out that fuzzy logic control, neural networks and specialist system are three typical intelligent control methods. Specialist system faces many problems and difficulties, such as knowledge acquirement depends on manual transplantation which leads to high cost and low efficiency; applying general clarified set brings forward with feeble reasoning ability, matching collision and combination explosion. Nowadays modern intelligent control focuses on fuzzy logic and neural networks or the combination of the two. Especially the combination of fuzzy logic and neural networks has already become the hotspot in intelligent control research because it absorbs the strongpoint while overcomes the weak point of both.
This paper aims at the two main limitations of pure fuzzy system in traditional modeling. One is the difficulties in extracting and adjusting the fuzzy rules and subject functions, the other is the workload increasing exponentially along with the number of variables. So we improve the old regular combination arithmetic on the base of normal fuzzy neural networks (NFNN). Through the arithmetic, we can assemble and combine fuzzy rules gained by neural networks automatically. Furthermore we test its effectiveness by functional simulation experiment.
Converter steel-making is a complicated diverse high temperature reaction process and the foundation of the model also is a diverse non-linear mapped process. So it is emergent to find a more superior control model abandoned the tradition static model in the condition that many medium and small converter cannot use an assistant gun.
Moreover, we apply NFNN in the problems of static controlling modeling of steel-making and abstract vanadium by converter and come up with the refrigerant adding sub-model. The simulated result accounts for the usefulness and practicability of the modeling method and the regular combination once again.
引文
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