The Sixth International Symposium on Neural Networks (ISNN 2009) (häftad)
Häftad (Paperback / softback)
Antal sidor
2009 ed.
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Wang, Hongwei (ed.), Shen, Yi (ed.), Huang, Tingwen (ed.), Zeng, Zhigang (ed.)
181 schwarz-weiße Tabellen 310 schwarz-weiße Abbildungen
181 Tables, black and white; XXVIII, 904 p.
234 x 155 x 51 mm
1385 g
Antal komponenter
1 Paperback / softback
The Sixth International Symposium on Neural Networks (ISNN 2009) (häftad)

The Sixth International Symposium on Neural Networks (ISNN 2009)

Häftad Engelska, 2009-05-08
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This volume of Advances in Soft Computing and Lecture Notes in Computer th Science vols. 5551, 5552 and 5553, constitute the Proceedings of the 6 Inter- tional Symposium of Neural Networks (ISNN 2009) held in Wuhan, China during May 26-29, 2009. ISNN is a prestigious annual symposium on neural networks with past events held in Dalian (2004), Chongqing (2005), Chengdu (2006), N- jing (2007) and Beijing (2008). Over the past few years, ISNN has matured into a well-established series of international conference on neural networks and their applications to other fields. Following this tradition, ISNN 2009 provided an a- demic forum for the participants to disseminate their new research findings and discuss emerging areas of research. Also, it created a stimulating environment for the participants to interact and exchange information on future research challenges and opportunities of neural networks and their applications. ISNN 2009 received 1,235 submissions from about 2,459 authors in 29 co- tries and regions (Australia, Brazil, Canada, China, Democratic People's Republic of Korea, Finland, Germany, Hong Kong, Hungary, India, Islamic Republic of Iran, Japan, Jordan, Macao, Malaysia, Mexico, Norway, Qatar, Republic of Korea, Singapore, Spain, Taiwan, Thailand, Tunisia, United Kingdom, United States, Venezuela, Vietnam, and Yemen) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews by the Program Committee members and reviewers, 95 high-quality papers were selected to be published in this volume.
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  3. Analysis and Control of Output Synchronization for Complex Dynamical Networks

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Session 1: Theoretical Analysis.- The Initial Alignment of SINS Based on Neural Network.- Analysis on Basic Conceptions and Principles of Human Cognition.- Global Exponential Stability for Discrete-Time BAM Neural Network with Variable Delay.- The Study of Project Cost Estimation Based on Cost-Significant Theory and Neural Network Theory.- Global Exponential Stability of High-Order Hopfield Neural Networks with Time Delays.- Improved Particle Swarm Optimization for RCP Scheduling Problem.- Exponential Stability of Reaction-Diffusion Cohen-Grossberg Neural Networks with S-Type Distributed Delays.- Global Exponential Robust Stability of Static Reaction-Diffusion Neural Networks with S-Type Distributed Delays.- A LEC-and-AHP Based Hazard Assessment Method in Hydroelectric Project Construction.- A Stochastic Lotka-Volterra Model with Variable Delay.- Extreme Reformulated Radial Basis Function Neural Networks.- Research of Nonlinear Combination Forecasting Model for Insulators ESDD Based on Wavelet Neural Network.- Parameter Tuning of MLP Neural Network Using Genetic Algorithms.- Intelligent Grid of Computations.- Method of Solving Matrix Equation and Its Applications in Economic Management.- Efficient Feature Selection Algorithm Based on Difference and Similitude Matrix.- Exponential Stability of Neural Networks with Time-Varying Delays and Impulses.- Session 2: Machine Learning.- Adaptive Higher Order Neural Networks for Effective Data Mining.- Exploring Cost-Sensitive Learning in Domain Based Protein-Protein Interaction Prediction.- An Efficient and Fast Algorithm for Estimating the Frequencies of 2-D Superimposed Exponential Signals in Presence of Multiplicative and Additive Noise.- An Improved Greedy Based Global Optimized Placement Algorithm.- An Alternative Fast Learning Algorithm of Neural Network.- Computer Aided Diagnosis of Alzheimer's Disease Using Principal Component Analysis and Bayesian Classifiers.- Margin-Based Transfer Learning.- Session 3: Support Vector Machines and Kernel Methods.- Nonlinear Dead Zone System Identification Based on Support Vector Machine.- A SVM Model Selection Method Based on Hybrid Genetic Algorithm and Empirical Error Minimization Criterion.- An SVM-Based Mandarin Pronunciation Quality Assessment System.- An Quality Prediction Method of Injection Molding Batch Processes Based on Sub-Stage LS-SVM.- Soft Sensing for Propylene Purity Using Partial Least Squares and Support Vector Machine.- Application of Support Vector Machines Method in Credit Scoring.- Session 4: Pattern Recognition.- Improving Short Text Clustering Performance with Keyword Expansion.- Nonnative Speech Recognition Based on Bilingual Model Modification at State Level.- Edge Detection Based on a PCNN-Anisotropic Diffusion Synergetic Approach.- Automatic Face Recognition Systems Design and Realization.- Multi-view Face Detection Using Six Segmented Rectangular Features.- Level Detection of Raisins Based on Image Analysis and Neural Network.- English Letters Recognition Based on Bayesian Regularization Neural Network.- Iris Disease Classifying Using Neuro-Fuzzy Medical Diagnosis Machine.- An Approach to Dynamic Gesture Recognition for Real-Time Interaction.- Dynamic Multiple Pronunciation Incorporation in a Refined Search Space for Reading Miscue Detection.- Depicting Diversity in Rules Extracted from Ensembles.- A New Statistical Model for Radar HRRP Target Recognition.- Independent Component Analysis of SPECT Images to Assist the Alzheimer's Disease Diagnosis.- The Multi-Class Imbalance Problem: Cost Functions with Modular and Non-Modular Neural Networks.- Geometry Algebra Neuron Based on Biomimetic Pattern Recognition.- A Novel Matrix-Pattern-Oriented Ho-Kashyap Classifier with Locally Spatial Smoothness.- An Integration Model Based on Non-classical Receptive Fields.- Classification of Imagery Movement Tasks for Brain-Computer Interfaces Using Regression Tree.- MIDBSCAN: An Efficient Density-Based Clustering Algorithm.- Detect