Recognition and Detection Methods of Artificial Intelligence in Computer Network Faults under the Background of Big Data

Abstract

With the widespread use of computers and the rapid development of Internet technology, computer application technology has become more and more important in people’s work and life. The article mainly studies particle swarm optimization (PSO) and radial basis neural network function (RBF). Particle swarm optimization is an evolutionary swarm intelligence algorithm, such as nonderivative node transfer function or gradient information loss. Because its principle is simple and easy to implement, it can deal with some problems that cannot be solved by traditional methods. It is widely used in neural networks, and has achieved good results in many fields such as network training, performance optimization, and system mismanagement. RBF neural network is a feedforward neural network, which overcomes the shortcomings of traditional neural network learning process that the convergence is highly dependent on the initial value and can only be partially converged. This paper organically combines PSO algorithm and RBF neural network to study the detection and detection of computer network faults. The results show that although the prediction error of the improved network model on the experimental test set is still only 89.3% different from that of the SVM model, its convergence time is reduced to 0.90699885 s, which can effectively detect and identify computer network faults.