A new kind of recurrent neural network is presented for solving the Lyapunov equation with time-varying coefficient matrices. Different from other neural-computation approaches, the neural network is developed by following Zhang et al.'s design method, which is capable of solving the time-varying ...
For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN...
For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN...
The paper presents a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy neural network (TRFNN) adaptive inverse modeling control for a class of nonlinear discrete-time time-delay systems. This type of controller uses a TRFNN as an adaptive inverse modeling controller. TRFNN is a recurrent fuzzy neu...
S. Wang, “A Wiener-Type recurrent neural network and its control strategy for nonlinear dynamic applications,” Journal of Process Control, vol. 19, no. 6, pp. 942-953, 2009.Y.-L. Hsu and J.-S. Wang, "A Wiener-type recurrent neural network and its control strategy for nonlinear ...
This paper presents a TSK-type recurrent fuzzy neural network (TRFNN) system and hybrid algorithm to control nonlinear uncertain systems. The TRFNN is modified from the RFNN to obtain generalization and fast convergence rate. The consequent part is replaced by linear combination of input variables...
Recurrent Neural Networks have loops in them, allowing information to persist. The input is represented as x_t In the figure above, we see part of the neural network,A,processing some input x_t and outputs h_t. A loop allows information to be passed from one step to the next. ...
15.Stability Analysis of a Class of Recurrent Neural Network Model;一类递归神经网络模型的稳定性研究 16.Dynamic Behavior Analysis of Several Classes of Neural Network Models;几类神经网络模型的动力学行为研究 17.Qualitative Analysis of a Class of Neural Network Model with Two Neurons;一类二元神经网络模...
基于长短记忆型递归神经网络的行人属性预测方法 Pedestrian attributes prediction method based on the length of the memory-type recurrent neural network本发明属于智能监控领域,为提出行人属性预测方法,准确地预测出行人的各精细化属性,具有更强的灵活性和多样性. The present invention is in the field of ...
For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN...