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Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

机译:基于神经网络的机械运动方案多层次模糊评估模型

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摘要

To implement a quantificational evaluation for mechanical kinematic scheme more effectively, a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly, the structure of evaluation model is constructed according to evaluation indicator system . Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result, as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model, the corresponding evaluation result is outputted and the best alternative can be selected. Under this model, expert knowledge can be effectively acquired and expressed , and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system . Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation .
机译:为了更有效地对机械运动方案进行量化评估,利用神经网络和模糊理论提出了一种多层次,多目标的评估模型。首先,根据评价指标体系构建评价模型的结构。然后生成评估样本并提供以训练该模型。这样就可以反映出属性值与评价结果之间的关系,以及评价指标的权重。一旦对每个候选人的评估指标进行模糊量化并输入到训练后的网络模型中,就输出相应的评估结果,并可以选择最佳替代方案。在该模型下,可以有效地获取和表达专家知识,并且可以通过多级评价指标体系对运动学方案进行量化评价。讨论了该模型的几个关键问题,并举例说明该模型是可行的,可以作为解决运动学方案评估的新思路。

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