Neural Information Processing: Research and Development (eBook, PDF)

Redaktion: Rajapakse, Jagath Chandana; Wang, Lipo

• Format: PDF

Produktbeschreibung

This monograph presents a careful collection of recent research and developments in the field of neural information processing. This includes investigations in the functioning and engineering of biological neural networks and applications of artificial neural networks for solving real-world problems. The book is organized in three parts, architectures, learning algorithms and applications, with a variety of different examples and case studies from different fields such as the visual system, object detection, financial time series prediction, the auditory cortex, and robot manipulator control.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 478
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783540399353
• Artikelnr.: 53194016

Inhaltsangabe

1: Architectures.- Scale Independence in the Visual System.- Dynamic Neuronal Information Processing of Vowel Sounds in Auditory Cortex.- Convolutional Spiking Neural Network for Robust Object Detection with Population Code using Structured Pulse Packets.- Networks Constructed of Neuroid Elements Capable of Temporal Summation of Signals.- Predictive Synchrony Organized by Spike-Based Hebbian Learning with Time-Representing Synfire Activities.- Improving Chow-Liu Tree Performance by Mining Association Rules.- A Reconstructed Missing Data-Finite Impulse Response Selective Ensemble (RMD-FSE) Network.- Higher Order Multidirectional Associative Memory with Decreasing Energy Function.- Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes.- 2: Learning Algorithms.- On Some External Characteristics of Brain-like Learning and Some Logical Flaws of Connectionism.- Superlinear Learning Algorithm Design.- Extension of Binary Neural Networks for Multi-class Output and Finite Automata.- A Memory-Based Reinforcement Learning Algorithm to Prevent Unlearning in Neural Networks.- Structural Optimization of Neural Networks by Genetic Algorithm with Degeneration (GAd).- Adaptive Training for Combining Classifier Ensembles.- Combination Strategies for Finding Optimal Neural Network Architecture and Weights.- 3: Applications.- Biologically Inspired Recognition System for Car Detection from Real-Time Video Streams.- Financial Time Series Prediction Using Non-Fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression.- A Method for Applying Neural Networks to Control of Nonlinear Systesm.- Robot Manipulator Control via Recurrent Neural Networks.- Gesture Recognition Based on SOM Using Multiple Sensors.- Enhanced Phrase-Based Document Clustering Using Self-Organizing Map (SOM) Architectures.- Discovering Gene Regulatory Networks from Gene Expression Data with the Use of Evolving Connectionist Systems.- Experimental Analysis of Knowledge Based Multiagent Credit Assignment.- Implementation of Visual Tracking System Using Artificial Retina Chip and Shape Memory Alloy Actuator.