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分析了灰色预测方法和支持向量机各自的优缺点,提出了将二者相结合的一种新的预测模型-灰色支持向量机预测模型。为了提高预测精度,用粒子群算法对灰色支持向量机的相关初始化参数进行优化,用优化后的模型对汽车制动系统故障进行预测与诊断。实验结果表明文章所提出的预测模型有效可靠,为提高预测精度提供了新的途径。
The advantages and disadvantages of gray forecasting method and support vector machine are analyzed respectively. A new forecasting model, gray support vector machine forecasting model, which combines the two is proposed. In order to improve the prediction accuracy, particle swarm optimization algorithm is used to optimize the initialization parameters of gray support vector machine, and the optimized model is used to predict and diagnose the fault of vehicle braking system. The experimental results show that the prediction model proposed in this paper is effective and reliable, which provides a new way to improve the prediction accuracy.