ะะทะดะฐัะตะปัััะฒะพ 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 ComputingNeural 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