Abstract:

According to the shape features of wind shear images extracted by wavelet invariant moment based on cubic B-spline wavelet basis, an improved Genetic Algorithm (GA) was proposed to apply to the type recognition of microburst, low-level jet stream, side wind shear and tailwind-or-headwind shear. In the improved algorithm, the adaptive crossover probability only considered the number of generation and mutation probability just emphasized the fitness valve of individuals and group, so that it could control the evolution direction uniformly, and greatly maintain the population diversity simultaneously. Lastly, the best feature subset chosen by the improved genetic algorithm was fed into 3-nearest neighbor classifier to classify. The experimental results show that it has a good direction and be able to rapidly converge to the global optimal solution, and then steadily chooses the critical feature subset in order to obtain a better performance of wind shear recognition that the mean recognition rate can reach more than 97% at last.

Key words: wavelet moments, wind shear, shape features, genetic algorithm, type recognition

摘要：

针对采用三次B样条小波矩提取的低空风切变图像的形状特征，提出了一种改进的遗传算法(GA)用于微下击暴流、低空急流、侧风以及顺逆风4种风切变的类型识别中。该算法中自适应交叉概率仅考虑了进化代数的影响，而变异概率强调个体与群体适应度的作用，使得在均匀把握群体演变方向时，极大程度地丰富种群的多样性。对由此改进算法选取的最优特征子集，采用三阶近邻分类器进行分类识别。实验结果表明，该自适应遗传算法操作方向性强，能快速收敛到全局最优解，稳定地提取出最优特征子集，最终使低空风切变的平均识别率达到97%以上,获取了较好的识别效果。

关键词: 小波矩, 风切变, 形状特征, 遗传算法, 类型识别