一种新的机电设备状态趋势智能混合预测模型

针对机电设备运行状态受多因素影响，变化趋势复杂，难以用单一预测方法进行有效预测的问题，提出一种新的基于改进灰色系统一支持向量机一神经模糊系统的智能混合预测模型。该模型首先利用改进灰色系统弱化数据序列波动性、支持向量机处理小样本和模糊神经系统处理非线性模糊信息的优点，分别进行趋势预测，然后通过改进遗传算法对这三者的预测结果进行自适应加权组合。将该模型应用于信号随机波动性较强、趋势变化复杂的标准算例和某机组振动趋势的预测中，研究结果表明，该模型的预测性能均优于上述三种单一预测方法。

NEW INTELLIGENT HYBRID PREDICTION MODEL FOR CONDITION TREND OF ELECTROMECHANICAL EQUIPMENT

Due to the fluctuation and complexity of electromechanical equipment operation condition affected by various factors, it is difficult to use a single prediction method to accurately describe its moving trend. So a new hybrid prediction model based on improved grey system, support vector machine (SVM) and neuro-fuzzy system is proposed. In this model, the fluctuation of the data sequence is weakened by the improved grey system, the SVM can deal with small samples and neuro-fuzzy system is capable of processing non-linear fuzzy information. The hybrid prediction model combines these advantages and its prediction result is an adaptive combination of these single method's via improved genetic algorithms. This model was applied to the trend prediction of a fluctuant and complicated benchmark data and a vibration trend signal from machine sets. Testing results show that the prediction performance of this model outperforms any one of the three prediction methods.