VAV系统送风管道静压的线性神经元网络补偿控制
Compensation control of supply duct’s static-pressure using linear neural network in VAV systems
摘要：
利用神经元网络具有自学习以及超强非线性逼近的能力，提出了基于线性神经元网络的补偿控制方法。这种控制方法能够根据送风管道静压耦合因素的变化自适应地调节控制量，实现对管道静压的补偿控制。给出了神经元权系数的在线学习方法，并通过实验验证了控制算法的有效性。
关键词：变风量,神经元网络,线性神经元补偿控制,静压
Abstract：
Based on the self-learning and nonlinearity approximation ability of linear neural network, puts forward a compensation control method. This method can achieve compensation control by adjusting the variable adaptively according to the variation of coupled elements. Presents the online learning method of neuron weights and validates it with an experiment.
Keywords:variableairvolume，neuralnetwork，linearneuralcompensatingcontrol，staticpressure