In the field of economic research, most of the sample data is not obtained based on controllable experiments but generated during the normal operation of the economic system. Therefore, the change of an economic variable is usually not caused by a single change of a cause variable. It is the result of a combination of multiple factors. Therefore, it is necessary to study the application of mathematical intelligent computing in computer intelligent manufacturing system. The purpose of this paper is to explore the application of mathematical intelligent computing in computer intelligent manufacturing system. For this reason, this paper uses the furnace temperature control model to carry out simulation experiment. In this simulation experiment, three algorithms of mathematical intelligent computing are mainly used, including BPES intelligent computing method, genetic algorithm, and MARS algorithm. The research results show that the superparameter optimization based on MARS has high efficiency, and the best result, the worst result, the average result, the variance, and the average time of multiple independent runs are controlled below 0.03 s. In this experiment, when the hidden layer node is 9, the prediction error value is the smallest, and the model simulation curve is basically consistent with the measured curve trend. In the simulation experiment of this paper, these three algorithms have shown good results in their respective links.

中文翻译：

---

数学经济模型在制造业金融系统中的应用

在经济研究领域，大部分样本数据不是基于可控实验获得的，而是在经济系统正常运行过程中产生的。因此，一个经济变量的变化通常不是由一个原因变量的单一变化引起的。它是多种因素综合作用的结果。因此，有必要研究数学智能计算在计算机智能制造系统中的应用。本文旨在探讨数学智能计算在计算机智能制造系统中的应用。为此，本文采用炉温控制模型进行仿真实验。本次仿真实验主要使用了数学智能计算的三种算法，包括BPES智能计算方法、遗传算法和MARS算法。研究结果表明，基于MARS的超参数优化效率高，将最佳结果、最差结果、平均结果、方差、多次独立运行的平均时间控制在0.03 s以下。本实验中，当隐藏层节点为9时，预测误差值最小，模型仿真曲线与实测曲线趋势基本一致。在本文的仿真实验中，这三种算法在各自的环节都表现出了良好的效果。多次独立运行的平均时间控制在0.03s以下。本实验中，当隐藏层节点为9时，预测误差值最小，模型仿真曲线与实测曲线趋势基本一致。在本文的仿真实验中，这三种算法在各自的环节都表现出了良好的效果。多次独立运行的平均时间控制在0.03s以下。本实验中，当隐藏层节点为9时，预测误差值最小，模型仿真曲线与实测曲线趋势基本一致。在本文的仿真实验中，这三种算法在各自的环节都表现出了良好的效果。