针对海杂波中弱信号的检测问题,提出了一种新颖的再生核模糊系统。该系统巧妙地和再生核函数的性质结合起 来,使其结构及参数辨识具有严格的数学理论基础并且变得简单。该系统隶属度函数具有语义特征,参数学习只限于前 件, 并且避免了非线性数值算法和陷入局部极小值, 具有全局收敛性。利用该系统对Lorenz系统及真实海杂波进行仿真 实验,结果表明该系统能有效检测出混沌背景下的弱信号,比传统的支持向量机及神经网络方法具有更高的精度。

A novel fuzzy system whose fuzzy membership functions are the reproducing kernel functions is established to detect weak signals from a chaotic clutter by employing the approximating property of the reproducing kernel functions, which can generate the best structure automatically and avoid falling into local minimum with global convergence. Parameter learning is restricted to the antecedent. The proposed fuzzy system is applied to the estimation of the weak signal in chaotic clutter. The results show that the weak signals in chaotic clutter are accurately detected, which demonstrates the effectiveness of the systems.