Издательство InTech, 2012, -302 pp.The first section illustrates some general concepts of artificial neural networks, their properties, mode of training, static training (feedforward) and dynamic training (recurrent), training data classification, supervised, semi-supervised and unsupervised training.
Recurrent Neural Networks (RNNs), are like other ANN abstractions of biological nervous systems, yet they differ from them in allowing using their internal memory of the training to be fed recurrently to the neural network. This makes them applicable for adaptive robotics, speech recognition, attentive vision, music composition, hand-writing recognition, etc. There are several types of RNNs, such as Fully recurrent network, Hopfield network, Elman networks and Jordan networks, Echo state network, Long short term memory network, Bi-directional RNN, Continuous-time RNN, Hierarchical RNN, Recurrent multilayer perceptron, etc. In this section, some of these types of RNN are discussed, as well as application of each type.Part 1 Soft Computing
Neural Networks and Static Modelling
A Framework for Bridging the Gap Between Symbolic and Non-Symbolic AI
Ranking Indices for Fuzzy Numbers
Neuro-Fuzzy Digital Filter
Part 2 Recurrent Neural Network
Recurrent Neural Network with Human Simulator Based Virtual Reality
Recurrent Neural Network-Based Adaptive Controller Design for Nonlinear Dynamical
BRNN-SVM: Increasing the Strength of Domain Signal to Improve Protein Domain Prediction Accuracy
Recurrent Self-Organizing Map for Severe Weather Patterns Recognition
Centralized Distributed Parameter Bioprocess Identification and I-Term Control Using Recurrent Neural Network Model
Optimization of Mapping Graphs of Parallel Programs onto Graphs of Distributed Computer Systems by Recurrent Neural Network
Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics
Artificial Intelligence Techniques Applied to Electromagnetic Interference Problems Between Power Lines and Metal Pipelines

An Application of Jordan Pi-Sigma Neural Network for the Prediction of Temperature Time Series Signal

Издательство InTech, 2012, -302 pp.第一节说明了人工神经网络的一些一般概念、它们的特性、训练模式、静态训练（前馈）和动态训练（递归）、训练数据分类、监督、半监督和无监督训练。

递归神经网络（RNN），就像生物神经系统的其他ANN抽象，然而它们与之不同的是，允许使用其内部的训练记忆，以递归方式输入神经网络。这使得它们适用于自适应机器人技术、语音识别、专注的视觉、音乐创作、手写识别等。有几种类型的RNN，如完全递归网络、Hopfield网络、Elman网络和Jordan网络、回声状态网络、长短期记忆网络、双向RNN、连续时间RNN、层次RNN、递归多层感知器等。在本节中，将讨论其中一些类型的RNN，以及每种类型的应用。第一部分 软计算

神经网络和静态建模

弥合符号和非符号人工智能之间的差距的框架

模糊数的排名指数

神经-模糊数字滤波器

第二部分 递归神经网络

基于人类模拟器的虚拟现实的递归神经网络

基于递归神经网络的非线性动力学自适应控制器设计

BRNN-SVM：增加领域信号强度以提高蛋白质领域预测的准确性

用于恶劣天气模式识别的递归自组织地图

使用递归神经网络模型的集中分布式参数生物过程识别和I-Term控制

递归神经网络将并行程序图谱映射到分布式计算机系统图谱的优化研究

使用递归神经网络、嵌入式沃尔特拉和高阶统计对成人和胎儿心电图的检测和分类

人工智能技术应用于电力线和金属管道之间的电磁干扰问题

乔丹 Pi-Sigma 神经网络在温度时间序列信号预测中的应用

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