利用响应面法对非承载式车身的前车架进行零件厚度的优化,在限制加速度最大峰值及总质量的情况下最大化吸收动能。针对零件数目较多的情况,采用两步构造响应面进行变量筛选、优化的方法,首先对所有零件构造较为粗糙的响应面模型,用以筛...利用响应面法对非承载式车身的前车架进行零件厚度的优化,在限制加速度最大峰值及总质量的情况下最大化吸收动能。针对零件数目较多的情况,采用两步构造响应面进行变量筛选、优化的方法,首先对所有零件构造较为粗糙的响应面模型,用以筛选出关键零件及非关键零件;然后对关键零件构造较为精细的响应面,在此基础上对其进行尺寸优化。对加速度采用径向基函数(radial based function,RBF)响应面,有效提高响应面的精度。采用正交设计和均匀设计的方法选取试验点,能用较少的试验点构造出满足要求的响应面。结果表明,该方法对汽车车架的耐撞性能优化具有明显的效果,同时计算代价较低。展开更多
This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the...This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons： the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron： the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.展开更多
提出了一种基于自适应模糊系统的径向基高斯函数系统辨识方法,与传统的系统辨识和仿真方法相比,更具有精确性与智能性.RBF(radial based function)网络在逼近能力、分类能力和学习速度上均有优势.自适应神经-模糊推理系统(ANFIS)混合学...提出了一种基于自适应模糊系统的径向基高斯函数系统辨识方法,与传统的系统辨识和仿真方法相比,更具有精确性与智能性.RBF(radial based function)网络在逼近能力、分类能力和学习速度上均有优势.自适应神经-模糊推理系统(ANFIS)混合学习算法减少了原始纯反向传播算法搜索空间的维数,故收敛速度非常快.根据 ANFIS和RBF的特点,将它们结合起来,形成了基于自适应模糊系统的径向基高斯函数网络的系统辨识方法.展开更多
文摘利用响应面法对非承载式车身的前车架进行零件厚度的优化,在限制加速度最大峰值及总质量的情况下最大化吸收动能。针对零件数目较多的情况,采用两步构造响应面进行变量筛选、优化的方法,首先对所有零件构造较为粗糙的响应面模型,用以筛选出关键零件及非关键零件;然后对关键零件构造较为精细的响应面,在此基础上对其进行尺寸优化。对加速度采用径向基函数(radial based function,RBF)响应面,有效提高响应面的精度。采用正交设计和均匀设计的方法选取试验点,能用较少的试验点构造出满足要求的响应面。结果表明,该方法对汽车车架的耐撞性能优化具有明显的效果,同时计算代价较低。
基金supported by the National Natural Science Foundation of China (Grant No. 60375012)
文摘This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons： the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron： the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.
文摘提出了一种基于自适应模糊系统的径向基高斯函数系统辨识方法,与传统的系统辨识和仿真方法相比,更具有精确性与智能性.RBF(radial based function)网络在逼近能力、分类能力和学习速度上均有优势.自适应神经-模糊推理系统(ANFIS)混合学习算法减少了原始纯反向传播算法搜索空间的维数,故收敛速度非常快.根据 ANFIS和RBF的特点,将它们结合起来,形成了基于自适应模糊系统的径向基高斯函数网络的系统辨识方法.
