In this paper, the branches of recursive and recurrent neural networks are classified in detail according to the network structure, training objective function and learning algorithm implementation. They are roughly divided into three categories: The first category is General Recursive and Recurrent Neural Networks, including Basic Recursive and Recurrent Neural Networks, Long Short Term Memory Recursive and Recurrent Neural Networks, Convolutional Recursive and Recurrent Neural Networks, Differential Recursive and Recurrent Neural Networks, One-Layer Recursive and Recurrent Neural Networks, High-Order Recursive and Recurrent Neural Networks, Highway Networks, Multidimensional Recursive and Recurrent Neural Networks, Bidirectional Recursive and Recurrent Neural Networks; the second category is Structured Recursive and Recurrent Neural Networks, including Grid Recursive and Recurrent Neural Networks, Graph Recursive and Recurrent Neural Networks, Temporal Recursive and Recurrent Neural Networks, Lattice Recursive and Recurrent Neural Networks, Hierarchical Recursive and Recurrent Neural Networks, Tree Recursive and Recurrent Neural Networks; the third category is Other Recursive and Recurrent Neural Networks, including Array Long Short Term Memory, Nested and Stacked Recursive and Recurrent Neural Networks, Memory Recursive and Recurrent Neural Networks. Various networks cross each other and even rely on each other to form a complex network of relationships. In the context of the development and convergence of various networks, many complex sequence, speech and image problems are solved. After a detailed description of the principle and structure of the above model and model deformation, the research progress and application of each model are described, and finally the recursive and recurrent neural network models are prospected and summarized.

翻译：本文根据网络结构、训练目标函数及学习算法实现，对递归与循环神经网络分支进行了详细分类。大致分为三类：第一类为通用递归与循环神经网络，包括基础递归与循环神经网络、长短期记忆递归与循环神经网络、卷积递归与循环神经网络、微分递归与循环神经网络、单层递归与循环神经网络、高阶递归与循环神经网络、高速公路网络、多维递归与循环神经网络、双向递归与循环神经网络；第二类为结构化递归与循环神经网络，包括网格递归与循环神经网络、图递归与循环神经网络、时序递归与循环神经网络、晶格递归与循环神经网络、层次化递归与循环神经网络、树结构递归与循环神经网络；第三类为其他递归与循环神经网络，包括阵列长短期记忆、嵌套与堆叠递归与循环神经网络、记忆增强递归与循环神经网络。各类网络相互交叉甚至相互依存，形成复杂的关联网络。在各类网络发展与融合的背景下，诸多复杂序列、语音及图像问题得以解决。在详细阐述上述模型及其变体的原理与结构后，本文对各模型的研究进展与应用进行了论述，最后对递归与循环神经网络模型进行了展望与总结。