论文标题
基于完全连接的神经网络的多价分类的人工智能系统用于建筑管理
Artificial intelligence system based on multi-value classification of fully connected neural network for construction management
论文作者
论文摘要
这项研究致力于解决该问题，以确定建筑管理人员使用人工智能系统的专业自适应能力。提出了完全连接的馈送前向前向神经网络体系结构并进行了经验建模以创建数据集。人工智能系统的模型允许在执行专业领域的多价分类过程中评估完全连接的前馈神经网络中的过程。已经为机器学习模型的培训过程开发了一种方法，该方法反映了人工智能系统的组成部分之间的内部连接，使其可以从培训数据中学习。为了训练神经网络，使用了35个输入参数和29个输出参数的数据集；集合中的数据量为936个数据线。神经网络训练的比例分别为10％和90％。这项研究的结果可用于进一步提高成功实现专业实现所需的知识和技能。
This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems.It is proposed Fully Connected Feed-Forward Neural Network architecture and performed empirical modeling to create a Data Set. Model of artificial intelligence system allows evaluating the processes in an Fully Connected Feed-Forward Neural Network during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to learn from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.
