Recurrent Neural Networks: Design And Applications (international Series On Computational Intelligence)
by Lakhmi C. Jain /
1999 / English / PDF
4.4 MB Download
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.
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.
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.
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.