论文标题
通过神经网络的投入输出数据实现双线性
Bilinear realization from input-output data with neural networks
论文作者
论文摘要
我们提出了一种将具有神经网络（NNS）优势的良好非线性（双线性）识别方法连接起来的方法。拟合双线性系统的主要挑战是从输入和输出测量值中准确恢复相应的马尔可夫参数。之后，可以采用与Isidori提出的类似的实现算法。新颖的步骤是，此处将NN用作构建输入输出（I/O）数据序列的替代数据模拟器。然后，经典实现理论用于构建双线性解释模型，该模型可以通过强大的模拟和控制设计进一步优化工程过程。
We present a method that connects a well-established nonlinear (bilinear) identification method from time-domain data with neural network (NNs) advantages. The main challenge for fitting bilinear systems is the accurate recovery of the corresponding Markov parameters from the input and output measurements. Afterward, a realization algorithm similar to that proposed by Isidori can be employed. The novel step is that NNs are used here as a surrogate data simulator to construct input-output (i/o) data sequences. Then, classical realization theory is used to build a bilinear interpretable model that can further optimize engineering processes via robust simulations and control design.
