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Automatic generating numerical control rule using genetic-based with multiple critical evaluation

机译:基于遗传学的多重评估,自动生成数值控制规则

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A method is proposed to automatically extract numerical control rules from the sensor data without the help of experts by means of a genetic algorithms (GA) based on multiplet critical evaluation to meeting different criteria. Every generated numerical rule is accumulated in a control table called a numerical rule-based controller. The numerical control table can be stored into the controller of embedded control system to construct a numerical rule-based embedded controller to meet real-time processing in the industry. The combination of multiple critics applies on the controller as fitness function of genetic learning in order to generate numerical rules to achieve multi-objective genetic process. Moreover, this paper apply an experimental design method which add a 'King strategy' to crossover operator of the standard GA in order to reduce the blindness of GA search processes and raise the convergence speed. An illustrative experiment is successfully made on the computer simulation. The experimental results reveal that the proposed approach is more efficient and more effective than the single objective.
机译:提出一种方法,以通过基于遗传算法(GA)来从传感器数据中自动提取来自传感器数据的数值控制规则,基于MultipleT算法以满足不同标准。每个生成的数值规则都累积在称为基于数值规则的控制器的控制表中。数值控制表可以存储到嵌入式控制系统的控制器中,以构建基于数值规则的嵌入式控制器,以满足行业中的实时处理。多个批评者的组合适用于控制器作为遗传学习的健身功能,以产生数值规则以实现多目标遗传过程。此外,本文应用了一种实验设计方法,将“王策略”添加到标准GA的交叉操作员,以减少GA搜索过程的失明并提高收敛速度。在计算机仿真上成功进行了一个说明性实验。实验结果表明,所提出的方法比单身目标更有效,更有效。

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