Abstract: When dealing with big data sets, there are problems such as long model training time and too many iterations of the algorithm with the Bayesian network discriminative learning method. By introducing exponential parameters, a weighted class-conditional Bayesian network parameter learning method of chaos quantum particle swarm is proposed. The method addresses the estimation of the parameters of generative learning by optimizing the log-likelihood function. Then, it initializes the parameters of discriminative learning utlizing the result of the generative learning. Finally, the conditional log-likelihood function is optimized by constructing the chaos quantum particle swarm optimization algorithm with the chaos map sequence. The weighted class condition Bayesian network classifier is used to classify the faults of liquid rocket engines. The simulation results show that the improved method has high classification accuracy and low misclassification rate. Compared with the quantum particle swarm and the particle swarm optimization method, the chaos quantum particle swarm optimization algorithm can effectively reduce the number of iterations of the algorithm and improve the efficiency of the algorithm.

Key words: Bayesian network, weighted discriminative parameter learning, quantum behave particle swarm, chaos map sequence