论文部分内容阅读
本文介绍了广泛用于下料优化中的遗传算法和神经网络,并将这两种优化算法的结合用于家具生产板材下料中.实验采用了3650 mm (1850 mm的板材生产100套写字桌吊桶.该板材的利用率达到94.14%,运算时间仅用了35秒.实验结果证明,用遗传算法训练神经网络权值的方法可提高板材利用率并减少神经网络的搜索时间,同时可以提高神经网络的全局搜索能力。表1参5“,”This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm (1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly increasing the probability of finding a global optimum.