This papers proposes a test and recognition method of genetic BP neural network on common digital modulation modes in RFID standard.
本文对RFID标准中常用的数字调制方式提出了一种基于遗传BP神经网络的测试识别方法。
On the basis of evolutionary neural network, the recognition model of promoter in eukaryote's gene was built using BP algorithms and genetic algorithms.
所以在此基础上，利用进化神经网络，采取BP算法和遗传算法建立了真核生物基因启动子识别模型。
Combined Genetic Algorithms (ga) and back-propagation neural network (BP), an optimized GA-BP model was established to predict phosphorus content. Some data were chosen to train the network model.
结合遗传算法(GA)和误差反馈型神经网络(BP)，建立了优化的GA - BP神经网络预测模型，预测转炉炼钢过程钢液终点磷含量。
Sinter quality simulating model and optimizing iron ores matching model were built by using BP neural network technology and genetic algorithms technology, respectively.
应用BP神经网络技术和遗传优化技术分别建立烧结矿质量模拟模型和烧结寻优配矿模型；
Sinter quality simulating model and optimizing iron ores matching model were built by using BP neural network technology and genetic algorithms technology, respectively.
应用BP神经网络技术和遗传优化技术分别建立烧结矿质量模拟模型和烧结寻优配矿模型；
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