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首页> 外文期刊>International journal of modeling, simulation and scientific computing >Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis
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Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis

机译：模拟退火遗传算法优化BP神经网络在故障诊断中的应用

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摘要

Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight training algorithm in neural network application, the back propagation (BP) neural network learning algorithm combined with simulated annealing genetic algorithm (SAGA) is put forward. The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations. The simulated annealing mechanism is incorporated into the Genetic Algorithm (GA) to optimize the design and optimization of neural network conformation and network weights simultaneously. The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process, also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon. The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm. The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.
机译：最佳权重通常是通过固定网络构型在神经网络中获得的，这会影响网络的实用性。针对神经网络应用中构象设计和权重训练算法的不足，提出了结合模拟退火遗传算法（SAGA）的BP神经网络学习算法。使用分层编码方案和遗传运算来执行神经网络拓扑和网络权重的多点遗传优化。将模拟退火机制并入遗传算法（GA）中，以同时优化神经网络构象和网络权重的设计和优化。 SAGA利用GA的出色能力来掌握搜索过程的整体能力，还使用SA算法来控制算法的收敛性，从而避免过早出现现象。以一架直升机机载电气控制箱和飞机自动驾驶仪某飞行控制箱的故障诊断为测试平台，对该算法进行了仿真。仿真结论表明，该算法收敛速度快，诊断准确性高。

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