基于混沌优化的板形信号模式识别的研究

针对传统的基于最小二乘法板形信号模式识别方法抗千扰能力差、精度低，神经网络识别方法在实际应用中效果不佳的问题，通过对板形信号和板形识别数学模型的分析，首先将板形信号模式识别过程转化为函数的优化问题。为提高板形信号识别的精度和速度，以勒让德正交多项式作为板形缺陷的基模式，用模糊识别理论与混沌优化方法对该函数进行优化求解。采用模糊理论作为初步识别，用以降低混沌优化的求解维数和缩小搜索空间，借助梯度下降法的思想对混沌优化的局部搜索能力进行改善，从而进一步提高了混沌优化对板形信号模式识别的识别速度和精度。

Research on Pattern Recognition of Shape Signal Based on Chaotic Optimization

In practical application,the performance of the traditional least squares shape pattern recognition method is poor,its precision is low,and the results of the neural network recognition method is not good enough as well.By analyzing the shape signal and mathematical model of shape recognition,a new pattern recognition method of shape signal is presented.The chaotic optimization and fuzzy identification optimization method are built to improve the accuracy and speed of shape pattern recognition,and fuzzy recognition based on Legendre polynomial as a preliminary identification of the optimal solution is used to reduce the dimension of chaos and narrow the search space.The gradient descent algorithm is used to improve the chaos optimization algorithm.And the chaos optimization results are further improved.