...基于模糊神经网络卡车路段行程时间实时预测模型,阐述了自适应神经网络模糊系统(Adaptive Network-based Fuzzy Inference System,ANFIS)网络原理和方法对行程时间预测的可行性和可靠性,采用最小二乘法和误差反传算法结合的混合学习算法,减少了...
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...预测不仅要考虑负荷本身的历史时间序列,而且与气象因素密切相关,自适应神经网络模糊系统(Adaptive Neuro-Fuzzy Inference Sysrem,ANFIS)模型是一种有效的预测方法,而系统输入变量的合理性选择是影响预测效果的关键所在.
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当获得了足够的数据后，通过自适应神经网络模糊系统ANFIS来训练产生隶属度函数和模糊规则，即产生模糊控制器。
When obtaining plenty data, self-adapt neural network fuzzy control system ANFIS come into being subjection degree function and fuzzy rule, namely come into being fuzzy controller.
提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法。
A novel direct adaptive fuzzy neural networks (FNNs) controller for a class of uncertain nonlinear chaotic system is presented.
该文提出一种用于复杂的非线性未知系统辨识的混合神经网络模型—自适应模糊神经网络(AFNN)。
This paper presents a compound neural network model, i. e., adaptive fuzzy neural network (AFNN), which can be used for identifying the complicated nonlinear system.
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