Advances in Neural Networks  -- ISNN 2010 (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
757
Utgivningsdatum
2010-05-21
Upplaga
2010.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
3Kwok, James
Illustrationer
185 Illustrations, black and white; XXIX, 757 p. 185 illus.
Volymtitel
Part I
Dimensioner
234 x 155 x 28 mm
Vikt
1090 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783642132773
Advances in Neural Networks  -- ISNN 2010 (häftad)

Advances in Neural Networks -- ISNN 2010

7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I

Häftad Engelska, 2010-05-21
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This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled "Advances in Neural Networks- ISNN 2010, Part 1," covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled "Advance in Neural Networks ISNN 2010, Part 2" covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.
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Neurophysiological Foundation.- Stimulus-Dependent Noise Facilitates Tracking Performances of Neuronal Networks.- Range Parameter Induced Bifurcation in a Single Neuron Model with Delay-Dependent Parameters.- Messenger RNA Polyadenylation Site Recognition in Green Alga Chlamydomonas Reinhardtii.- A Study to Neuron Ensemble of Cognitive Cortex ISI Coding Represent Stimulus.- STDP within NDS Neurons.- Synchronized Activities among Retinal Ganglion Cells in Response to External Stimuli.- Novel Method to Discriminate Awaking and Sleep Status in Light of the Power Spectral Density.- Current Perception Threshold Measurement via Single Channel Electroencephalogram Based on Confidence Algorithm.- Electroantennogram Obtained from Honeybee Antennae for Odor Detection.- A Possible Mechanism for Controlling Timing Representation in the Cerebellar Cortex.- Theory and Models.- Parametric Sensitivity and Scalability of k-Winners-Take-All Networks with a Single State Variable and Infinity-Gain Activation Functions.- Extension of the Generalization Complexity Measure to Real Valued Input Data Sets.- A New Two-Step Gradient-Based Backpropagation Training Method for Neural Networks.- A Large-Update Primal-Dual Interior-Point Method for Second-Order Cone Programming.- A One-Step Smoothing Newton Method Based on a New Class of One-Parametric Nonlinear Complementarity Functions for P 0-NCP.- A Neural Network Algorithm for Solving Quadratic Programming Based on Fibonacci Method.- A Hybrid Particle Swarm Optimization Algorithm Based on Nonlinear Simplex Method and Tabu Search.- Fourier Series Chaotic Neural Networks.- Multi-objective Optimization of Grades Based on Soft Computing.- Connectivity Control Methods and Decision Algorithms Using Neural Network in Decentralized Networks.- A Quantum-Inspired Artificial Immune System for Multiobjective 0-1 Knapsack Problems.- RBF Neural Network Based on Particle Swarm Optimization.- Genetic-Based Granular Radial Basis Function Neural Network.- A Closed-Form Solution to the Problem of Averaging over the Lie Group of Special Orthogonal Matrices.- A Lower Order Discrete-Time Recurrent Neural Network for Solving High Order Quadratic Problems with Equality Constraints.- A Experimental Study on Space Search Algorithm in ANFIS-Based Fuzzy Models.- Optimized FCM-Based Radial Basis Function Neural Networks: A Comparative Analysis of LSE and WLSE Method.- Design of Information Granulation-Based Fuzzy Radial Basis Function Neural Networks Using NSGA-II.- Practical Criss-Cross Method for Linear Programming.- Calculating the Shortest Paths by Matrix Approach.- A Particle Swarm Optimization Heuristic for the Index Tacking Problem.- Structural Design of Optimized Polynomial Radial Basis Function Neural Networks.- Convergence of the Projection-Based Generalized Neural Network and the Application to Nonsmooth Optimization Problems.- Two-Dimensional Adaptive Growing CMAC Network.- A Global Inferior-Elimination Thermodynamics Selection Strategy for Evolutionary Algorithm.- Particle Swarm Optimization Based Learning Method for Process Neural Networks.- Interval Fitness Interactive Genetic Algorithms with Variational Population Size Based on Semi-supervised Learning.- Research on One-Dimensional Chaos Maps for Fuzzy Optimal Selection Neural Network.- Edited Nearest Neighbor Rule for Improving Neural Networks Classifications.- A New Algorithm for Generalized Wavelet Transform.- Neural Networks Algorithm Based on Factor Analysis.- IterativeSOMSO: An Iterative Self-organizing Map for Spatial Outlier Detection.- A Novel Method of Neural Network Optimized Design Based on Biologic Mechanism.- Research on a Novel Ant Colony Optimization Algorithm.- A Sparse Infrastructure of Wavelet Network for Nonparametric Regression.- Information Distances over Clusters.- Learning and Inference.- Regression Transfer Learning Based on Principal Curve.- Semivariance Criteria for Quantifying the Choice among Uncertain Outcomes.- Enhanced Extreme Learnin