| 摘要: |
| 根据样本空间的内积特性，提出一种无需迭代学习内积神经网络。以某型航空发动机的机载记录数据为例，对发动机进行了建模，结果表明该方法具有自学习速度快、抗干扰能力强、准确性高的特点。 |
| 关键词: 航空发动机 模型研究 数学模型 网络 |
| DOI： |
| 分类号:V233.75 |
| 基金项目: |
|
| THE SET UP OF NEURAL NETWORK IDENTIFICATION MODEL FOR AEROENGINE USING INNER PRODUCT PRINCIPLE |
|
Xie Shousheng1, Fan Siqi2
|
|
1.Dept of Aeroengine Engineering,Northwestern Polytechnical Univ.,Xi′an,710072;2.Xie Shousheng Fan Siqi(Dept of Aeroengine Engineering,Northwestern Polytechnical Univ.,Xi′an,710072
|
| Abstract: |
| According to the inner product feature of sample space,a new neural network which does not require to learn iteratively,is set up for a aeroengine in terms of data recorded on a plane.The results show that the new method has the advantage of faster self taught ability,higher accuracy,stronger anti interference ability and less maintenance work. |
| Key words: Aircraft engine Model study Mathematical model Network |
