Thirteen chapters summarize the design and application of recurrent neural networks (RNN), and exemplify current research ideas and challenges in this subfield of artificial neural network research and development. The first section concentrates on ideas for alternate designs and advances in theoretical aspects of RNNs. Some authors discuss aspects of improving RNN performance and connections with Bayesian analysis and knowledge representation. The second section looks at recent applications of RNNs, such as trajectories, control systems, robotics, and language learning. Architectures and learning techniques are addressed in every chapter. Annotation c. Book News, Inc., Portland, OR (booknews.com)
With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks.
This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects.
The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.
