模糊规则在神经网络预测模型中的应用前景

在采用径向基函数神经网络(RBFN)对太阳能发电系统输出功率进行预测的模型中,可以明确日照强度的精度对整个预测系统的精度起到了决定性的作用。通过在RBFN模型中引入模糊规则,改善云量数据的精准度,进而提高预测模型的精度。仿真结果表明,加入了模糊规则的模型,预测曲线更为近似。在全面考虑模糊的基础上,有可能提高预测精度。同时也证明了该方法可用于实际应用。

The Application Prospect of Fuzzy Rules in Neural Network Prediction Models

In the model using Radial Basis Function Neural Network (RBFN) to predict the output power of solar power generation systems, it can be clearly understood that the accuracy of solar radiation intensity has a decisive impact on the accuracy of the entire prediction system. By introducing fuzzy rules into the RBFN model, the accuracy of cloud data was improved, thereby improving the accuracy of the prediction model. The simulation results show that the prediction curve is more approximate with the addition of fuzzy rules to the model. Therefore, based on the comprehensive application of fuzzy rules, the prediction accuracy will be improved, which also proves that this method can be used in practical use.