摘要
将神经网络与模糊逻辑集成一驾驶适应性模糊神经综合测评系统。该系统含有一个Bp网 ,一个模糊推理机和一个知识库。以高速判断 ,跟踪 ,暗适应 ,选择反应 ,注意力 ,速视 ,动视力和深度知觉这 8个指标值为特征量构成学习样本 ,使用K 均值法对实验样本进行初始分类 ,形成标准学习样本 ,使用这些样本对所建系统进行训练和调试。利用经调试训练后的系统 ,依据所测驾驶员的心理、心理参数对驾驶员的驾驶适应性进行评价。试验表明 :其评价效果是令人满意的。
A comprehensive evaluation expert system of driver proneness is established in this paper. The system inchudes a Bp network, a fuzzy inferring engine and a knowledge library. Eight index values (which are high speed judgement, speed tracing, dark adaptation, tracking, selective response, attention, kinesthetic vision and depth perception) are used as characteristic parameters to form the testing samples. Using k?means method cluster the testing samples beforehand, standard learning samples are formed and then are used to train and adjust the system. The experiment proves that the evaluation effect of driver proneness is satisfying when we use the trained system to evaluate the driver proneness with driver′s mental and physical parameter.
出处
《公路交通科技》
CAS
CSCD
北大核心
2000年第6期76-79,共4页
Journal of Highway and Transportation Research and Development
基金
高等学校重点实验室访问者基金资助
关键词
驾驶员
适应性
神经网络
模糊逻辑
综合评判
Driver proneness
Neural network
Fuzzy logic
Comprehensive judging
