Journal of Interconnection Networks

To study the nonlinear characteristics simulation and experiment of multimode driving of intelligent vehicle, an improved neural network PID controller is proposed to realize the safe and active braking requirements of cruise vehicles, according to the study of vehicle dynamics of different active braking forms and the joint simulation of road condition, driving condition, and braking mechanical structure. On the basis of the current research, the four-layer neural network combined with PID control is proposed in this paper, which can better control the braking compared with the traditional PID controller and BP neural network, safety, efficiency and stability have been improved in the following cars. According to the animation simulation demonstration of CAR SIM software, it can be determined that the improved neural network PID controller in the mechanical system can realize the safety and active braking requirements of cruise vehicle, and it is more accurate and efficient than the traditional BP neural network PID controller. Relevant parameters of the existing millimeter-wave radars are set by the radar sensors, and the long-distance horizontal detection range is plus or minus 10 degrees. The radar is installed on the longitudinal axis of the front bumper and has a height of 442mm. According to Car SIM’s preset radar sensor module parameters, the sensors can independently collect and transmit relevant data, it is also possible to cooperate with other sensors for the same variable parameter to increase the accuracy and avoid abnormal errors caused by the failure of a certain sensor. Compared with the traditional PID controller and BP neural network, the combination of four-layer neural network and PID control can carry out better braking control and improve the safety, efficiency, and stability of the following cars.

To study nonlinear distortion image recognition technology. Through the study of neural networks, an image recognition model based on BP neural network is proposed: An improved algorithm for driving quantity factor. According to the established neural network model, 10 commonly used images of Arabic numeral characters are recognized. The effectiveness of the model is verified by experiments with the extracted feature parameters of the target image. The results show that 38 of the 40 distorted images with noise can be correctly identified and 2 of them can be incorrectly identified by the single-stage recognition network, and the recognition rate reaches 95%; the recognition rate of cascade network reaches 100%. Therefore, the BP network which drives the number term can accelerate the training time of the network and improve the recognition efficiency of the system.

In order to solve the problem that the test time is long and the test efficiency is affected in the process of IC test. With the increase in the complexity of integrated circuits, it is difficult now to diagnose the faults. To overcome this situation, there is a need to upgrade the test strategies. Based on the fault probability model, the order of test types and test vector is being adjusted. To improve the test efficiency, the high-quality test types and test vectors are loaded first, and the fault circuits are hit earlier. A hierarchical dynamic method for IC test flow is proposed. The Bayesian probability model was established by counting the failure rates of each test type and each test vector in the sample integrated circuit, and the loading sequence of each test vector was adjusted according to the probability of hitting the fault point. As the test progresses, the test data are collected constantly, the test failure rates of test type and test vector are dynamically updated, and the loading sequence of test type and test vector is adjusted synchronously. It is proved that the final circuit test time is reduced to 32.172s by the dynamic adjustment method, and the test time is reduced by 53.9%. The use of dynamically adjusted test process can find the fault circuit earlier, significantly reduce the test time of the fault circuit, and improve the test efficiency.

To explore the partition optimization and stability of telecommunication network under nonlinear control, a routing optimization algorithm for power control of distribution communication network is proposed, which is based on the Ad-hoc On-demand Distance Vector (AODV) routing optimization algorithm, based on the energy consumption model of wireless sensor network, the optimal routing mechanism for power control is constructed, and the optimal routing mechanism is used to select the route with low energy consumption to solve the problem of energy limitation of wireless sensor nodes. This paper aims at the network routing selection problem in the terminal information collection of the distribution network and designs the routing optimization algorithm of the wireless sensor network. Experimental results show that the model accuracy of the optimized model is 99.8%, the average residual error of the model is about 5.5nm, the model accuracy meets the requirements, and the method is effective. According to the confidence interval prediction results of wireless communication link reliability, a node power control system structure of wireless sensor network is proposed, and an optimal control algorithm of communication link quality and reliability of microgrid wireless sensor network based on fuzzy control is studied.

To improve the effect of depth information extraction of moving targets in the network, a nonlinear strategy-oriented method is proposed. With the advancement of science and technology, especially in wireless networks, a large amount of data is provided to people every hour of every day. Hence, it can increase the demand for data analysis tools. Nonlinear system modeling by using rough set theory to extract valuable information from large amounts of information, and then through the analytic hierarchy process (ahp) to determine the effect of input factors, then use particle swarm optimization algorithm (PSO) to find the accurate function, and USES the adaptive and population catastrophe and vaccine algorithm to make it to the local optimum, to achieve the aim of the complex. The experimental results show that, compared with M2 and M1 for 30 groups of samples, the model obtained by using M2 has a better fitting effect on the actual curve. The error of M2 is within ±3%, and the error of M1 is within ±6%, and the error is relatively large. The accuracy of the proposed method is higher than that of the neural network method, which proves that the nonlinear strategy is effective in the actual target depth information extraction.