共查询到18条相似文献,搜索用时 328 毫秒
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从质子交换膜燃料电池(PEMFC)实际应用的角度出发,采用Elman动态神经网络对PEMFC系统进行建模,以实验中采样到的PEMFC系统的工作温度输入输出数据训练网络,并采用动态反向传播学习算法根据误差不断调整网络参数直至达到要求精度。设计了一种适应模糊神经网络控制器,根据经验确定了初始隶属度函数和模糊规则,并采用自适应学习算法不断调整隶属度函数与模糊规则参数,使控制系统获得理想的输出。仿真实验以Elman神经网络模型为参考模型,使用自适应神经网络控制算法取得了较好的控制效果。总之,所设计的控制系统适合于控制PEMFC这样一类复杂非线性系统。 相似文献
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为解决传统控制方法在火力发电机组蒸汽温度控制过程中存在的强非线性、大迟延的难题,提出了一种基于长短期记忆(LSTM)神经网络在线估计和粒子群(PSO)滚动优化的预测控制算法。该方法将常规串级控制系统的主回路控制器用预测控制器替代,采用LSTM神经网络建立主蒸汽温度控制系统的过程模型,通过多步预测实现了对复杂非线性系统模型的精确预测。利用PSO算法在线求解主蒸汽温度控制系统的最优预测控制律,避免了传统递推方法无法直接求解非线性优化问题。仿真结果表明:与传统主蒸汽温度串级控制策略相比,该控制算法明显改善了控制系统的快速性,抗扰能力较强,对主蒸汽温度这类具有非线性及模型不精确的被控对象有一定的参考价值。 相似文献
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在热力系统中采用容错控制技术,可以提高控制系统的可靠性,同时降低运行成本。研究采用自适应模糊控制实现非失效的被动容错控制。提出一种新的自适应模糊控制器;采用基于动态神经元网络的辨识模型学习被控对象的动态特性,设计了评价模糊控制器性能的FITAE指标;采用遗传算法优化模糊控制器参数,同时优化比例因子和控制规则表。为了提高模糊控制器的自适应调整速度,采用二阶段优化策略。仿真研究表明:自适应模糊控制器的热力系统被动容错控制方法是可行的。图3参7 相似文献
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An artificial neural network can work as an intelligent controller for nonlinear dynamic systems through learning, as it can easily accommodate the nonlinearities and time dependencies. In dealing with complex problems, most common neural networks have some drawbacks of large training time, large number of neurons and hidden layers. These drawbacks can be overcome by a nonlinear controller based on a generalized neuron (GN) which retains the quick response of neural net. Results of studies with a GN-based power system stabilizer on a five-machine power system show that it can provide good damping over a wide operating range and significantly improve the dynamic performance of the system. 相似文献
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提出一种自适应学习率记忆递归神经网络预测控制器及自适应学习率方法,它由用于预测和控制的子神经网络组成,预测子网络向控制子网络提供控制灵敏度信号;并证明了记忆递归神经网络学习的收敛性和稳定性条件.仿真结果表明控制器在线实时控制具有非线性、时变、多变量特性的水轮发电机组,对各种工况具有良好的性能. 相似文献
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Hong-Chan Chang Mang-Hui Wang 《Energy Conversion, IEEE Transaction on》1995,10(2):339-347
An efficient self-organizing neural fuzzy controller (SONFC) is designed to improve the transient stability of multimachine power systems. First, an artificial neural network (ANN)-based model is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the ANN model. With the excellent learning capability inherent in the ANN, the traditional heuristic fuzzy control rules and input/output fuzzy membership functions can be optimally tuned from training examples by the backpropagation learning algorithm. Considerable rule-matching times of the inference engine in the traditional fuzzy system can be saved. To illustrate the performance and usefulness of the SONFC, comparative studies with a bang-bang controller are performed on the 34-generator Taipower system with rather encouraging results 相似文献
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Artificial neural network (ANN), in comparison with PID controllers which have broad applications in the highly complex HVAC systems, has recently received more attention. The present paper includes thermodynamic modeling of an evaporative condenser under steady state and transient state conditions for establishing control of thermal capacity, using Artificial neural network. To train the system under dynamic condition, predictive neural network, capable of understanding dynamic behavior and predicting the preset output is used. The principle operation of such neural networks is based on the reduction of gradients of errors existing between the predicted output and the actual output of the system. To control the system thermal capacity, neural controller based on training received from the reduction of gradients between the output controller and the ideal output, is used. Results obtained during present investigation indicate that artificial neural network controller is suitable substitute for PID controllers for thermal systems. 相似文献
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神经网络自学习PID控制器的研究 总被引:5,自引:0,他引:5
从兼顾人工神经网络控制系统自学习能力和实时性角度出发,通过构建新的人工神经网络自学习PID控制器的结构,控制系统的样本拾取,更新与优化均采用在线学习方式,使控制系统具有较强的自学习能力。在分析了目前广为应用的多层前向神经网络误差反向传播算法(BP)的局限性及原因的基础上,提高了改进的BP算法-自适应动量项BP算法。从而提高了神经网络的收敛速度和收敛精度,并通过实例验证了自学习PID控制器的可行性及改进算法的合理性。图6参4 相似文献