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A Hybrid Intelligent Learning Algorithm to Identify the ECNS Based on FBP Optimized by GA

机译:一种混合智能学习算法,用于基于GA优化FBP的ECN

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摘要

—Along with the development of computer network, the electronic commerce has become the new pattern to carry on the commercial activity gradually, but the security problem is also getting more and more prominent. How to identify the E-commerce network security (ECNS) rating and establish a security convenient application environment for the electronic commerce has already become a major concern topic that needs to be settled urgently. To identify the ECNS rating scientifically and accurately, this paper proposes a hybrid intelligent learning algorithm which uses the genetic algorithm (GA) to optimize the fuzzy backpropagation (FBP) neural network. The algorithm not only can exert the unique advantages of BP neural network (BPNN), but also overcome the shortcoming to produce the local minimum points in the network modeling process and enhance the accuracy of network security identification greatly. The ECNS identification results for 14 E-commerce systems show that the method is reliable and efficiency.
机译:- 随着计算机网络的发展,电子商务已成为新的模式逐步进行商业活动,但安全问题也越来越突出。如何识别电子商务网络安全(ECNS)评级并建立安全方便的电子商务的应用环境已经成为需要紧急解决的主要关注点。为了科学准确地识别ECNS评级,本文提出了一种混合智能学习算法,它使用遗传算法(GA)来优化模糊反向衰减(FBP)神经网络。该算法不仅可以发挥BP神经网络(BPNN)的独特优势,而且还克服了网络建模过程中的遗迹,并大大提高了网络安全识别的准确性。 14个电子商务系统的ECN识别结果表明该方法可靠且效率。

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