2 edition of **Neural control engineering** found in the catalog.

Neural control engineering

Steven J. Schiff

- 367 Want to read
- 22 Currently reading

Published
**2012** by MIT Press in Cambridge, MA .

Written in English

- Nonlinear Dynamics,
- Computational neuroscience,
- Robotics,
- Brain,
- Nonlinear control theory,
- Neural Networks (Computer),
- Neuroscience,
- Neurological Models,
- Physiology

**Edition Notes**

Includes bibliographical references and index.

Statement | Steven J. Schiff |

Series | Computational neuroscience series, Computational neuroscience |

Classifications | |
---|---|

LC Classifications | QP357.5 S35 2012 |

The Physical Object | |

Pagination | p. ; |

ID Numbers | |

Open Library | OL25008165M |

ISBN 10 | 9780262015370 |

LC Control Number | 2010036051 |

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With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field.

The book will serve as an essential guide for scientists in either biology or. With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field.

The book will serve as an essential guide for scientists in either biology or Cited by: Neural Network Control of Nonlinear Discrete-Time Systems (Automation and Control Engineering Book 21) - Kindle edition by Sarangapani, Jagannathan.

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Neural Control Engineering: The Emerging Intersection Between Control Theory and Neuroscience. Written for scientists and physicians in the fields of biology, physics, and engineering, this book presents the fundamentals of control theory and computational neuroscience.

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Inside, leading experts from diverse disciplinary groups. Neural Networks for Control highlights key issues in learning control and identifiesresearch directions that could lead to practical solutions for control problems in criticalapplication domains. It addresses general issues of neural network based control and neural networklearning with regard to specific problems of motion planning and control in robotics, and takes upapplication.

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The main theme seems to be applied optimization. The subtopics include dimensional reduction, machine learning, dynamics and control and reduced order methods. These were well chosen and well covered.". This book is intended for a wide audience— those professionally involved in neural network research, such as lecturers and primary investigators in neural computing, neural modeling, neural learning, neural memory, and neurocomputers.

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Specifically, we conduct research on novel neural technologies that can interact with the brain on a much finer scale and with greater coverage than previously. This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9–10,in Berlin.

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Both continuous- and discrete-time control systems are treated, although the emphasis is on continuous-time systems.

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“An adaptive and generalizable closed-loop system for control of medically-induced coma and other states of anesthesia”, Journal of Neural Engineering, 13(6), Nov.

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Early examples of control systems were developed actually before the science was fully understood. In this paper, a Backpropagation-Through-Time Neural Controller (BTTNC) developed for active control of structures under dynamic loadings is presented. The BTTNC consists of two components: (1) a Neural Emulator Network to represent the structure to be controlled; and (2) a Neural Action Network to determine the control action on the structure.

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The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based .AN INTRODUCTION TO THE USE OF NEURAL NETWORKS IN CONTROL SYSTEMS MARTIN T.

HAGAN1, HOWARD B. DEMUTH2 AND ORLANDO DE JESÚS1 1School of Electrical & Computer Engineering, Oklahoma State University, Stillwater, Oklahoma,USA 2Electrical & Computer Engineering Department, University of Colorado, Boulder, Colorado,USA.The two volumes set, CCIS andconstitutes the refereed proceedings of the 14th International Conference on Engineering Applications of Neural Networks, EANNheld on Halkidiki, Greece, in September The 91 revised full papers presented were carefully reviewed and selected from numerous submissions.