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本文从结构和算法上研究了Min-Max模糊神经网络,找出了其固有的局限性,相应地提出了一系列的改进措施,得到了好的仿真结果,并运用了它作为分类器对实际信号进行了分类,得到了满意的结果。由于网络参数对网络的性能影响很大,本文应用遗传算法对网络算法中的参数进行了优化处理,与优化前相比,网络性能有明显地提高。
In this paper, Min-Max fuzzy neural network is studied from the structure and algorithm, and its inherent limitations are found out. A series of improvement measures are put forward accordingly. Good simulation results are obtained and used as a classifier to actual The signals were classified and satisfactory results were obtained. Because of the influence of network parameters on the performance of the network, the genetic algorithm is used to optimize the parameters of the network algorithm. Compared with the pre-optimization, the performance of the network is obviously improved.