工控网络稳定性测试模型仿真

Test Model of Industrial Control Network Stability

在工业领域工控网络稳定性的研究中,由于受到环境中强干扰的冲击,传统算法在保持工控网络稳定的过程中,难以避免外界非线性的电磁干扰对工控网络稳定性造成的冲击,使工控系统面临着巨大的威胁。为解决上述问题,提出一种基于神经网络的工控网络稳定性测试模型。根据神经网络算法的特点建立工控网络稳定性测试模型,利用sigmoid函数进行工控网络稳定性测试模型中的非线性映射,利用误差最小的反向传递对权值进行修正,将工控网络稳定性测试模型进行神经网络训练,获得在线调整的加权系数,实现工控网络稳定性的自适应调整。实验结果表踢,利用改进算法能够保持工控网络稳定性。

Keep the industrial control network stability in the industrial field has an important value. Traditional algorithms in the process of stabilizing the industrial control network, it is difficult to avoid the nonlinear electromagnetic interference of the impact on the stability of industrial control network, so that the industrial control system is facing a great threat. A test model of stability for industrial control network is put forward based on neural network. The test model is established according to the characteristics of the neural network algorithm, the nonlinear mapping of the test model in obtained via the sigmoid function, the weights of the neural network are revised by using the minimum error reverse transfer, the test model is trained by the neural network, the weighting coefficients are got by online ad- justment, so as to realize the adaptive adjustment of industrial control network stability. The experimental results show that the improved algorithm can keep the stability of industrial control network.