Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.

中文翻译：

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通过神经网络方法评估飞机驾驶舱情绪

研究表明，应用常规方法对飞机驾驶舱进行情绪评价存在不足。为了解决这一问题，本研究建立了一种更高效的座舱情绪评价系统，以简单快速地获得座舱情绪评价值。为此，应用神经网络构建情感模型，对飞机驾驶舱内部设计的情感预测进行评价。此外，应用多种技术和感性工程方法获取典型飞机模型的驾驶舱内部情感评价数据。在这方面，激进基函数神经网络（RBFNN）、埃尔曼神经网络（ENN）和一般回归神经网络（GRNN）被用于构建情感预测评估模型。然后，通过模型评价标准、网络结构、网络参数等因素对三种模型进行综合比较。得到的实验结果表明，与其他两种神经网络相比，GRNN不仅分类准确率最高，而且稳定性最高，是一种更适合飞机驾驶舱情绪评价的方法。本研究结果为驾驶舱内部空间的情感评估提供决策支持。得到的实验结果表明，与其他两种神经网络相比，GRNN不仅分类准确率最高，而且稳定性最高，是一种更适合飞机驾驶舱情绪评价的方法。本研究结果为驾驶舱内部空间的情感评估提供决策支持。得到的实验结果表明，与其他两种神经网络相比，GRNN不仅分类准确率最高，而且稳定性最高，是一种更适合飞机驾驶舱情绪评价的方法。本研究结果为驾驶舱内部空间的情感评估提供决策支持。