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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%.

With the development of society, people's requirements on living standards are constantly improving. As a convenient means of transportation, cars begin to enter people's life, which inevitably drives the growth of automobile demand and makes China's automobile industry show a rapid development trend. Rubber as an essential raw material in the automotive industry, its development prospects should not be underestimated. This article through to in the production of rubber used for rubber machine control, PLC and inverter as the core, combined with the Angle sensor, based on the fuzzy PID control algorithm for extruder for the easy to the problems that arise when a feed control system design, the control system for the glue supply machine, can make the continuous, rapid and stable feeding of the extruder. Make the quality of glue stable, improve the safety of production operation, improve the efficiency of production.

The vector control system of permanent magnet synchronous motor based on sliding mode observer (SMO) is studied in this paper. On the basis of analyzing the traditional sliding mode observer, an improved sliding mode observer is proposed. Firstly, by using the hyperbolic tangent function instead of the traditional symbol function, the chattering of the system is suppressed. Secondly, a low pass filter which has the variable cutoff frequency along with the rotor speed is designed to reduce the phase delay. Then, by using Kalman filter, it could make back EMF information more smoothly. Finally, in order to obtain accurate position and velocity information, the method of phase-locked loop (PLL) is proposed to estimate the position and speed of the rotor. The simulation results show that the new algorithm can not only improve the accuracy of the position and speed estimation of the rotor but reduce the chattering of the system.