基于模糊神经网络的产品可装配性评判

针对产品装配信息量大、影响因素复杂、许多参数依赖人的判断和推理等特点,以及多数现行评判方法存在的不足,提出了基于模糊神经网络的产品可装配性评判方法·该方法依据产品装配特征的类型,先构建产品可装配性信息数据库,再通过将常规的三层BP网络模糊化处理,得到输入和权值均已模糊化的产品可装配性模糊神经网络模型·按照预置的网络训练规则进行训练,获得最佳连接权值·经实例检验,证明这种模糊神经网络模型用于产品可装配性评判是可行的,且具有很好的稳定性·

Assembling Capacity Evaluation Based on FNN

An evaluation method is proposed on FNN basis for those products which are featured with a big amount of information on assembling process with complicated influencing factors and lots of parameters relying on human judgment/reasoning especially the deficiencies of most of existing evaluation methods to them. According to the characteristic type of a product to assemble, the method shall be done in such a sequence, i.e., construct a database of all assembly information; process fuzzily the conventional 3-level BP network; set up an FNN model for assembling capacity evaluation of which both the inputs and weights have been fuzzed up. Then the weight distribution for optimum neuronal continuity of assembling process are available after a training to the evaluation network in accordance to preset rules. An exemplification shows the applicability and stability of the FNN model.