Knowledge acquisition is the “bottleneck” of building an expert system. Based on the optimization model, an improved genetic algorithm applied to knowledge acquisition of a network fault diagnostic expert system is proposed. The algorithm applies operators such as selection, crossover and mutation to evolve an initial population of diagnostic rules. Especially, a self adaptive method is put forward to regulate the crossover rate and mutation rate. In the end, a knowledge acquisition problem of a simple network fault diagnostic system is simulated, the results of simulation show that the improved approach can solve the problem of convergence better.
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机译:基于优化遗传神经网络的井下运输机械滚动轴承故障诊断(The Application of Optimizing the GENETIC NEURAL NETWORK to the Fault Diagnosis of Rolling Bearings of Transporting Machinery Underground)