【摘要】 板形检测信息的模式分解是板形控制过程中的技术难点，该文提出的一种新的神经网络模式识别方法却可以解决这个难题。该识别方法的优点是：在ＡＲＴ网络的特征表示场中采用了具有正反馈和非线性变换的结构，能够有效地抑制板形检测数据中的干扰影响，提高了模式识别系统的抗干扰能力；在类别场中抛弃了传统的竞争学习机制，新的学习机制可以迅速分解板形模式；按照轧机执行机构板形控制的能力设置标准板形模式，可以对任意复杂形式的板形缺陷进行控制。用这种识别方法对实测板形进行了模式分解，识别结果完全正确，充分说明ＡＲＴ神经网络识别方法是一种理想的板形模式识别方法。更多还原

【Abstract】 Pattern decomposition of shape measurement is one of the difficult techniques in shape control system. A novel pattern recognition method based on neural network was presented in detail, its superiorities lie in the following points: positive feedback and nonlinear transforming structure are introduced in the representing field of ART neural network, so that the influences of disturbances exiting in shape measurements are rejected and in turn the system is improved in disturbance rejection. Traditional competitive learning mechnaism in the type field of ART neural network was abandoned and replaced by a new learning method, which is very quick at completing shape pattern decomposition; standard shape patterns are set up according to the shape control capability of the actuating units in the rolling mill, so that shape defects of any type can be controlled. When the new pattern recognition method is used in decomposing real shape measurements, completely correct results are obtained, this means that the recognition method based on ART neural network is an ideal shape recognition method.更多还原