基于自适应直觉模糊推理的目标识别方法

将自适应神经网络--直觉模糊推理系统(adaptive neuro intuitionistic fuzzy inference system, ANIFIS)引入信息融合领域，提出一种基于自适应直觉模糊推理的目标识别方法。首先，分析了现有目标识别方法的特点与局限性，建立了基于ANIFIS的Takagi Sugeno型目标识别模型。其次，设计了系统变量属性函数和推理规则，确定了各层输入输出计算关系及合成计算表达式。再次，设计了学习算法对网络和规则进行训练修改。最后，以20批典型目标的类型识别为例，分析比较基于直觉模糊推理及ANIFIS推理的输出结果与识别精度。仿真结果表明该方法是一种比较实用、有效的决策融合方法.

Techniques for target recognition based on adaptive intuitionistic fuzzy inference

To the issues of target recognition (TR), a technique for TR based on adaptive neuro intuitionistic fuzzy inference system (ANIFIS) is proposed with intuitionistic fuzzy inference—neural nets theory introduced into the area of information fusion. First, after analyzing the properties and vulnerabilities of the existing TR methods, ANIFIS is proposed. Moreover, because the logical system can be mapped a fuzzy multilayer feedforward nets system, a model for TR on ANIFIS with Takagi Sugeno type is established. Then, the attribute functions, i.e., membership and nonmembership functions, and the inference rules of the system variables are devised with computational relations between layers of input and output and a synthesized computational expression. Subsequently, a learning algorithm of neural net is devised to train net and modify rules. Finally, the output results and recognition precision based on two techniques, including intuitionistic fuzzy inference and ANIFIS, are analyzed and compared by providing TR instances with 20 typical targets. The simulated results show that it is a more practical and valid technique on decision making fusion which can improve recognition precision and training speed.