仿真结果表明，所设计的模糊神经网络控制器能有效地减少单交叉口平均车辆延误，具有较强的学习和泛化能力。

Results of simulation show that the fuzzy neural controller can not only decrease the average vehicle delay but also possesses excellent abilities of learning and generalization.

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目的针对铝电解故障发生机理及特点，提出采用层次模块化模糊神经网络实现对铝电解故障快速、有效检测的新方法。

This paper proposes a new method, which is a layer construction of Modular Fuzzy Neural Network according to the mechanism and characteristic of Aluminum Electrolysis Fault Diagnosis.

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与多元线性回归、模糊回归和自适应模糊神经网络相比，该模型学习精度高且具有较好的泛化能力，能取得较好的预测效果。

Comparing with the models based on multiple statistic analysis, generalized regress-ion neural network or adapted fuzzy neural network model, it shows better learning precision and generalization.

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