弧焊过程神经网络模糊控制

提出一种将FLC与神经网络技术相结合的方法对钨极氩弧焊（GTAW）过程进行控制，它克服了模糊规则产生对专家的依赖及模糊集非自适应性的问题。隶属函数的自适应及模糊规则的自组织通过神经网络的自学习和竞争获得。该方法实现了弧焊过程中模糊规则的自动确定和隶属度函数在线调度。 以GTAW过程焊缝几何参数调节为对象，验证了算法的有效性。计算机仿真表明，采用该方法的系统性能有较大的提高。

Neural Fuzzy Logic Control for Gas Tungsten Arc Welding

A novel technique, that combines the FLC and neural network (NN) techniques, to control the gas tungsten arc welding (GTAW) process is presented. This technique overcomes the limitations such as the dependency on the experts for fuzzy rule generation, the fuzzy set that is non adaptive, etc. The adaptation of membership function as well as the self organizing of fuzzy rule are realized by the self learning and competitiveness of the NN. This approach facilitates a mechanism for an automatic determination of the fuzzy rule and in process adaptation of membership function for an advanced welding process control. This is because a fixed membership function cannot guarantee the required system performance, as the arc welding process is a highly time variable system. Taking GTAW process welds bead width that regulates the system as the controlled plant, the proposed algorithm has been verified to be highly effective for an arc welding process. Computer simulations confirm that the characteristics of the system have improved notably when compared with a number of currently available methods.