Search

For the model parameters of RSSI signal intensity location algorithm in wireless sensor networks are susceptible to environmental impact, the positioning accuracy cannot meet the needs of high-precision indoor positioning in specific scenarios. This paper presents a Zigbee indoor location algorithm based on improved neural network optimization algorithm. Quantum particle swarm optimization (QPSO) is used to optimize the smooth parameters of the generalized neural network. The root mean square error between the predicted coordinates and the actual coordinates of the location node is selected to construct the fitness function. With the help of MATLAB software, the simulation comparison between the QPSO-GRNN localization algorithm and the non-optimized GRNN localization algorithm is established. The experimental results show that the average error of the non-optimized GRNN localization algorithm is 1. 0143m, and the average error of the QPSO-GRNN localization algorithm is 0. 5683m. The improved positioning algorithm has high accuracy, strong robustness and anti-jamming ability. Zigbee indoor positioning technology based on improved neural network optimization algorithm has high positioning accuracy and good stability, and has certain application value and development prospects.

In order to solve the shortcomings of traditional industrial control network intrusion detection schemes, such as insensitivity to detection samples and inaccurate judgment of internal anomalies. An industrial control network intrusion detection scheme based on FCM algorithm and supervised Kohonen is proposed. FCM algorithm, FCM-GRNN network algorithm, FCM-BP network algorithm, FCM-Kohonen network algorithm and FCM-S_ Kohonen network algorithm are built on MATLAB software platform to test DARPA data samples. The accuracy of clustering results of different types of intrusion is counted according to five algorithms. The scheme can detect NORMAL, U2R, R2L, DoS and PRB network attacks more accurately, and the overall average classification accuracy rate is more than 95%.