From practical application, adaptive fuzzy identification and control models of proton exchange membrane fuel cell (PEMFC) were developed based on input-output sampled data and experts' experience.
从质子交换膜燃料电池(PEMFC)实际应用的角度出发，应用自适应模糊神经网络技术对PEMFC系统进行建模与控制。
The response curves indicate that the adaptive fuzzy control is able to control the active output power and the reactive output power of the fuel cell.
仿真结果表明，该动态模型能够预测输出电压。响应曲线显示出自适应模糊控制算法能够较好控制燃料电池有功和无功功率的输出。
An equivalent fuel consumption model based on the overall working efficiency was built using a new adaptive control strategy.
在自适应控制策略的基础上建立了基于工作效率的等效油耗模型。
Air and fuel controlling of this stove are realized by PI controller, and temperature controlling adopts self adaptive genetic fuzzy controller.
热处理炉空气、燃料的控制均采用遗传算法优化的PI控制，炉体温度控制采用自适应的遗传模糊控制器。
The Control strategy based on the CMAC neural network and varying gain compensation has high accuracy and self-adaptive ability in engine air fuel ratio control.
基本CM AC神经网络和变增益补偿的控制策略在发动机空燃比控制中具有良好的精度和自适应性。
Combined with control characteristics of engine air fuel ratio, the CMAC controller which is self adaptive to compensate fuel injection volume is studied.
结合发动机空燃比的控制特征，研究了一种能自适应补偿喷油量的CMAC控制器。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
By means of an identified adaptive neural fuzzy inference system (ANFIS) model of the excess air factor, the simulation of static state air fuel ratio feed-forward control was carried out.
借助于辨识的过量空气系数自适应神经网络模糊推理系统(ANFIS)模型，进行了静态空燃比前馈控制仿真。
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