Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Funct... This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear ...
(2009). Towards real-world applications of online learning spiral recurrent neural networks. J. Intell. Learn. Syst. Appl. 1:1. doi: 10.4236/jilsa.2009.11001R. Sollacher and H. Gao, “Towards Real-World Applications of Online Learning Spiral Recurrent Neural Networks,” Journal of Intelligent...
摘要: of variations on these fundamental concepts, presenting ideas for more efficient and effective B. LEARNING IN RECURRENT NEURAL NETS Learning is a fundamental aspect of neural Theydiscuss continuous-time recurrent neural networks for solving linear and quadratic...
There are many types of neural networks, such as auto-encoders, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. They stand apart from one other through the type of process...
If you want to make sense of patterns in your data that changes with time, your best bet is a Recurrent Neural Network. RNNs remember the inputs and the context as they have internal memory, enabling users to have more flexibility in the types of data that models or networks can proces...
There are three different types of networks we use: recurrent neural networks, which use the past to inform predictions about the future; convolutional neural networks, which use ‘sliding’ bundles of neurons (we generally use this type to process imagery); and more conventional neural networks,...
2.Recurrent Neural Network (RNN) RNN是在NLP和语音处理中广泛应用和流行的算法。 RNN的一个主要问题是它对消失和爆炸梯度的灵敏度,换句话说,在训练过程中,由于大量大小导数的相乘,梯度可能会呈指数衰减或爆炸。随着时间的推移,这种敏感度会降低,这意味着网络会随着新输入的进入而忘记初始输入。
在地球物理学中,神经网络已被考虑用于modeling the dynamic process of seismic waveform inversion。为了模拟地震波传播,提出了一种理论指导的recurrent neural network(RNN); RNN 是专门为求解控制微分方程而设计的,其中一些参数被指定为控制物理方程中的物理参数。特别地,给定在时域离散化的波动方程,下一个时间步长的...
The proposed DCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) neural network in efficient learning mechanism, guaranteed system stability and dynamic response. The recurrent network is embedded in the DCMAC by adding feedback connections in the association ...
The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition....