Recurrent Neural Networks

The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim.

Book Description

The second part of the book consists of seven chapters, all of which are about system identification and control. The third part of the book is composed of Chapter 11 and Chapter 12, where two interesting RNNs are discussed, respectively.The fourth part of the book comprises four chapters focusing on optimization problems. Doing optimization in a way like the central nerve systems of advanced animals including humans is promising from some viewpoints.

Table of Contents

• Aperiodic (Chaotic) Behavior in RNN with Homeostasis as a Source of Behavior Novelty: Theory and Applications
• Biological Signals Identification by a Dynamic Recurrent Neural Network: from Oculomotor Neural Integrator to Complex Human Movements and Locomotion
• Linguistic Productivity and Recurrent Neural Networks
• Recurrent Neural Network Identification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes
• Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control
• Recurrent Fuzzy Neural Networks and Their Performance Analysis
• Recurrent Interval Type-2 Fuzzy Neural Network Using Asymmetric Membership Functions
• Rollover Control in Heavy Vehicles via Recurrent High Order Neural Networks
• A New Supervised Learning Algorithm of Recurrent Neural Networks and L2 Stability Analysis in Discrete-Time Domain
• Application of Recurrent Neural Networks to Rainfall-runoff Processes
• Recurrent Neural Approach for Solving Several Types of Optimization Problems
• Applications of Recurrent Neural Networks to Optimization Problems
• Neurodynamic Optimization: towards Nonconvexity
• An Improved Extremum Seeking Algorithm Based on the Chaotic Annealing Recurrent Neural Network and Its Application
• Stability Results for Uncertain Stochastic High-Order Hopfield Neural Networks with Time Varying Delays
• Dynamics of Two-Dimensional Discrete-Time Delayed Hopfield Neural Networks
• Case Studies for Applications of Elman Recurrent Neural Networks
• Partially Connected Locally Recurrent Probabilistic Neural Networks

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