| Citation: | Weixia GUI, Qian LU, Meili SU. A Firewoks Algorithm-Back Propagation Fault Diagnosis Algorithm for System-level Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1102-1109. doi: 10.11999/JEIT190484
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In order to diagnose fault units in the large-scale multiprocessor systems more quickly and accurately, a system-level fault diagnosis algorithm—FireWorks Algorithm-Back Propagation Fault Diagnosis (FWA-BPFD) based on fireworks algorithm and Back Propagation(BP) neural network is proposed. Firstly, two population strategy, cooperative operator and optimal operator are introduced into fireworks algorithm. A new fitness function is designed, and the mutation operator, mapping rule and selection strategy are optimized. Then, the optimization process of weight and threshold value in BP neural network is optimized by the self-regulating mechanism of global and local searching ability of fireworks algorithm. Simulation results show that compared with other algorithms, this algorithm not only reduces the number of iterations and training time, but also improves the accuracy of diagnosis.
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