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| NEURAL NETWORKS AND PATTERN RECOGNITION
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To order this title, and for more information, click here
By
Omid Omidvar, University of the District of Columbia
Judith Dayhoff, Institute of System Research, University of Maryland
Description
This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural
Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area
for neural networks technology. The contributors are widely known and highly respected researchers and practitioners in the field.
Audience
Researchers and practitioners in the fields of pattern recognition, neural networks, signal processing, control engineering, electrical engineering, industrial engineering, and mechanical engineering.
Contents
(Chapter Headings) Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield,Pulse-Coupled Neural Networks.H.
Li and J. Wang,A Neural Network Model for Optical Flow Computation.F. Unal and N. Tepedelenlioglu,Temporal Pattern Matching Using an
Artificial Neural Network.J. Dayhoff, P. Palmadesso, F. Richards, and D.-T. Lin, Patterns of Dynamic Activity and Timing in Neural Network
Processing. J. Ghosh, H.-J. Chang, and K. Liano, A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons.
P. Tito, B. Horne, C.L. Giles, and P. Collingwood, Finite State Machines and Recurrent Neural Networks--Automata and Dynamical Systems
Approaches. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error. A. Nigrin, Using SONNET 1 to Segment
Continuous Sequences of Items. K. Venkatesh, A. Pandya, and S. Hsu,On the Use of High Level Petri Nets in the Modeling of Biological
Neural Networks. J. Principe, S. Celebi, B. de Vries, and J. Harris, Locally Recurrent Networks: The Gamma Operator, Properties, and
Extensions.Preface. Contributors. J.L. Johnson, H. Ranganath, G. Kuntimad, and H.J. Caulfield, Pulse-Coupled Neural Networks: Introduction.
Basic Model. Multiple Pulses. Multiple Receptive Field Inputs. Time Evolution of Two Cells. Space to Time. LinkingWaves and Time Scales.
Groups. Invariances. Segmentation. Adaptation. Time to Space. Implementations. Integration into Systems. Concluding Remarks. References.
H. Li and J. Wang, A Neural Network Model for Optical Flow Computation: Introduction. Theoretical Background. Discussion on the Reformulation.
Choosing Regularization Parameters. A Recurrent Neural Network Model. Experiments. Comparison to Other Work. Summary and Discussion.
References. F. Unal and N. Tepedelenlioglu, TemporalPattern Matching Using an Artificial Neural Network: Introduction. Solving Optimization
Problems Using the Hopfield Network. Dynamic Time Warping Using Hopfield Network. Computer Simulation Results. Conclusions. References.
J. Dayhoff, P. Palmadesso, F. Richards, and D.-T. Lin, Patterns of Dynamic Activity and Timing in Neural Network Processing: Introduction.
Dynamic Networks. Chaotic Attractors and Attractor Locking. Developing Multiple Attractors. Attractor Basins and Dynamic Binary Networks.
Time Delay Mechanisms and Attractor Training. Timing of Action Potentials in Impulse Trains. Discussion. Acknowledgments. References.
J. Ghosh, H.-J. Chang, and K. Liano, A Macroscopic Model of Oscillation in Ensembles of Inhibitory and Excitatory Neurons: Introduction.
A Macroscopic Model for Cell Assemblies. Interactions Between Two Neural Groups. Stability of Equilibrium States. Oscillation Frequency
Estimation. Experimental Validation. Conclusion. Appendix. References. P. Tito, B. Horne, C.L. Giles, and P. Collingwood, Finite State
Machines and Recurrent Neural Networks--Automata and Dynamical Systems Approaches: Introduction. State Machines. Dynamical Systems. Recurrent
Neural Network. RNN as a State Machine. RNN as a Collection of Dynamical Systems. RNN with Two State Neurons. Experiments--Learning Loops
of FSM. Discussion. References. R. Anderson, Biased Random-Walk Learning: A Neurobiological Correlate to Trial-and-Error: Introduction.
Hebb's Rule. Theoretical Learning Rules. Biological Evidence. Conclusions. Acknowledgments. References and Bibliography. A. Nigrin, Using
SONNET 1 to Segment Continuous Sequences of Items: Introduction. Learning Isolated and Embedded Spatial Patterns. Storing Items with
Decreasing Activity. The LTM Invariance Principle. Using Rehearsal to Process Arbitrarily Long Lists. Implementing the LTM Invariance
Principle with an On-Center Off-Surround Circuit. Resetting Items Once They can be Classified. Properties of a Classifying System. Simulations.
Discussion. K. Venkatesh, A. Pandya, and S. Hsu, On the Use of High Level Petri Nets in the Modeling of Biological Neural Networks: Introduction.
Fundamentals of PNs. Modeling of Biological Neural Systems with High Level PNs. New/Modified Elements Added to HPNs to Model BNNs. Example
of a BNN: The Olfactory Bulb. Conclusions. References. J. Principe, S. Celebi, B. de Vries, and J. Harris, Locally Recurrent Networks:
The Gamma Operator, Properties, and Extensions: Introduction. Linear Finite Dimensional Memory Structures. The Gamma Neural Network.
Applications of the Gamma Memory. Interpretations of the Gamma Memory. Laguerre and Gamma II Memories. Analog VLSI Implementations of
the Gamma Filter. Conclusions. References.
| Bibliographic details |
Hardbound, 351 pages, publication date: OCT-1997
ISBN-13: 978-0-12-526420-4
ISBN-10: 0-12-526420-8
Imprint: ACADEMIC PRESS
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Last update: 7 Sep 2009
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