Pseudo-Random Recurrent Artificial Neural Networks for Control

伪随机递归人工神经网络（ANN）是一种新颖的概念，将用于控制领域。伪随机递归神经网络是在伪随机互连网络研究的基础上提出的一个新概念。伪随机神经网络不是使用全连接前馈或循环网络，而是使用伪随机图连接，伪随机图可以提供大量神经元的几乎随机连接，每个神经元上有少量链接。这个想法是使用大量的神经元来捕捉控制系统的非线性特性；然而，少量的链接使得实时实现成为可能。潜在的伪随机图，被称为Cayley图，是已知密度最大的图，这意味着对于给定的度和直径，它们具有最多的节点数。通过利用这些图的节点对称特性，可以开发出简单的同时向所有其他神经元广播信号的方法。结果是一个“类脑”人工神经网络，它有大量的神经元（成百上千）以一种几乎随机的方式连接在一起，连接数量相对较少。这些伪随机人工神经网络将采用诸如效用反向传播和启发式动态规划等强化控制策略来实现，用于一些现有的非线性控制应用。对不需要神经元完全连接的人工神经网络的研究，将为一种与生物对应物更相似的新形式的人工神经网络铺平道路。

Tang 9626655 A novel concept, the pseudo-random recurrent artificial neural networks (ANN) will be used in control applications. The Pseudo-random recurrent ANN is a novel concept derived from previous work on pseudo-random interconnection networks. Instead of using a fully connected feedforward or recurrent network, a pseudo-random ANN is connected with a pseudo-random graph that can provide almost random connections of lots of neurons with a small number of links at each neuron. The idea is to use the large number of neurons to capture the non-linear nature of the control system; and yet the small number of links makes real-time implementation feasible. The underlying pseudo-random graph, known as Cayley graphs have been the densest known graphs which means they have the largest number of nodes for a given degree and diameter. By exploiting the node-symmetric property of these graphs, simple simultaneous broadcasting their signals to all other neurons can be developed. The result is a "brain-like" ANN that have a large number of neurons (hundreds and thousands) connected in an almost random manner with a relatively small number of connections. These pseudo-random ANNs will be implemented with reinforcement control strategies such as the backpropagation of utility and the heuristic dynamic programming for some existing non-linear control applications. The investigation of ANNs that do not require complete connections of neurons will pave the way for a new form of ANN that has more resemblance to its biological counterpart.