A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction

Abstract

The fault prediction problem for modern industrial equipment is a hot topic in current research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm improves the step size and discovery probability. Then, it introduces the change trend of fitness function value into the step size update formula to balance the search speed and accuracy. At the same time, the algorithm initial global search step is larger, while the step size of the local search is smaller in the latter part of the algorithm. In the process of discovering the global optimal solution, the probability of preserving the offspring with good fitness is increased, and the uncertainty of preference random walk is improved. As the search progresses, the probability of discovery is reduced, which makes it easy to produce new individuals in the later stage of evolution. Based on this, a BP neural network prediction model based on dynamic cuckoo search algorithm optimization is established. And the experimental results show that the proposed prediction model has faster convergence and higher accuracy.