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Research on intelligent control strategy of plasma cutting process

机译:等离子切割工艺智能控制策略研究

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By taking into account these problems such as the nonlinear volt-ampere property of plasma arc, the multi-parameter close coupling of cutting technology process and the difficulty to determine the optimal parameter, we present a control strategy based on the combination of RBF neural network and expert system. The RBF neural network and expert system can resolve a certain tape problems respectively. Mutual combination can take full advantages of the high logical reasoning ability of expert system and the good robustness and a strong learning ability of neural network, overcoming the disadvantages of the expert system as poor fault tolerance and learning ability. This control algorithm avoids the redundant modeling process of nonlinear system, having the ability of multi-parameter decoupling. Expert system has high adaptive ability and self-learning ability so it can achieve parameters trained by neural network during the reasoning process and then it obtain optimal output value, having quite strong guidance to productive practice.
机译:考虑到等离子弧的非线性伏安特性,切削工艺过程的多参数紧密耦合以及确定最佳参数的难度等问题,提出了基于RBF神经网络组合的控制策略和专家系统。 RBF神经网络和专家系统可以分别解决某些磁带问题。相互结合可以充分利用专家系统的高逻辑推理能力,神经网络的良好鲁棒性和强大的学习能力,克服了专家系统的容错性和学习能力差的缺点。该控制算法避免了非线性系统的冗余建模过程,具有多参数解耦的能力。专家系统具有较高的自适应能力和自学习能力,可以在推理过程中获得神经网络训练得到的参数,然后获得最优的输出值,对生产实践具有很强的指导意义。

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