基于人工神经网络的联合收割机变速箱计算机辅助设计

Computer aided design of combine gearbox using artificial neural networks

针对自走式联合收割机变速箱优化设计中存在计算量大、图表多等问题,提出了一种新的神经网络学习算法,相对于其他学习算法,该算法侧重于网络参数的调整,通过对样本集的模糊推理、调整和分类学习来实现自适应的神经网络学习。通过BP网络的学习和训练,采用单输入双输出的1-8-2结构、1-6-2结构、1-4-2结构进行训练,从实际的应用效果来看,选择1-6-2的BP网络结构作为最终的神经网络形式,网络的识别精度是非常高的。结果表明,该算法能运用神经网络对联合收割机变速箱进行了设计研究,建立数学描述形式,分析了通过神经网络来实现变速箱设计模型构建的方法。研究表明,应用神经网络构建的模型能够减少系统的分析次数,并能够很大程度的提高模型的精度,满足计算要求,最终在设计空间内寻找出较好的设计方案。

The optimum design of the self-propelled combine gearbox showed some deficiencies such as too much calculation and too many charts. A new algorithm was proposed which improves the training of neural networks. Different from previous approaches, this new approach focuses on the samples, emphasizes particularly on parameter adjustment of networks. Via fuzzy deduction, adjustment of the samples and classified training, a better self-adaptive training performance was achieved. After learning and training of BP Artificial Neural Networks (ANN), single-input and double-output structures in the form of 1-8-2, 1-6-2, and 1-4-2 were adopted. According to practical effectiveness, 1-6-2 structure with high identification precision as the final BP ANN form is selected, and the identification precision of BP ANN is perfectly high. This approach based on artificial neural networks to design the combine gearbox, gave a mathematical description. The method establishing gearbox design model by neural networks was analyzed. Study shows that the neural networks model can reduce the frequency of system analysis, improve the precision of the model to a great degree, meet the requirements of calculation, and then find out an improved scheme from design space.