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Engineering Applications of Neural Networks 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings

:	Dominic Palmer-Brown, Chrisina Draganova, Elias Pimenidis, Haris Mouratidis
:	Engineering Applications of Neural Networks 11th International Conference, EANN 2009, London, UK, August 27-29, 2009, Proceedings
:	Springer-Verlag
:	9783642039690
:	1
:	CHF 75.80
:

:	Datenkommunikation, Netzwerke
:	English

:	508
:	DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book constitutes the refereed proceedings of the 11th International Conference on Engineering Applications of Neural Networks, EANN 2009, held in London, GB, on August 27-29, 2009. The 47 revised full papers were carefully reviewed and selected from many submissions. The following diverse topics were discussed: reducing urban concentration, entropy topography in epileptic electroencephalography, phytoplanktonic species recognition, revealing the structure of childhood abdominal pain data, robot control, discriminating angry and happy facial expressions, flood forecasting and assessing credit worthiness.

	Preface	5
	Organization	6
	Table of Contents	8
	Intelligent Agents Networks Employing Hybrid Reasoning: Application in Air Quality Monitoring and Improvement	13
	Introduction	13
	Aim of This Project	13
	Air Pollution	14
	Theoretical Background	14
	Risk Evaluation Using Fuzzy Logic	15
	The Agent-Based System	16
	System’s Architecture	16
	Sensor Agents Architecture	17
	Evaluation Agents	19
	System’s Agents	21
	Decision Agents	23
	Actuators	24
	Graphical User Interface	24
	Pilot Application of the System	25
	Running the Agent Network for Heat Index	25
	Running the Agent Network for Air Pollutants	26
	Conclusions	26
	References	27
	Neural Network Based Damage Detection of Dynamically Loaded Structures	29
	Introduction	29
	Methodology of Damage Detection	30
	Artificial Neural Network	32
	Stochastic Analysis	32
	Software Tools	33
	Application – Cantilever Beam	34
	Conclusions	37
	References	37
	Reconstruction of Cross-Sectional Missing Data Using Neural Networks	40
	Introduction	40
	Developing a New Algorithm	41
	The Modified GRNN (Generalized Regression Neural Networks) Algorithm	42
	The GMI Algorithm	42
	Assessing the New Technique	44
	Results and Discussion	45
	Summary and Conclusion	45
	References	46
	Municipal Creditworthiness Modelling by Kernel-Based Approaches with Supervised and Semi-supervised Learning	47
	Introduction	47
	Municipal Creditworthiness Problem Description	48
	Basic Notions of Support Vector Machines and Learning	49
	Modelling and Analysis of the Results	52
	Modelling by SVMs with Supervised Learning	52
	Modelling by Kernel-Based Approaches with Semi-supervised Learning	53
	Conclusion	55
	References	56
	Clustering of Pressure Fluctuation Data Using Self-Organizing Map	57
	Introduction	57
	Acquisition of Pressure Fluctuation Data	58
	Methodology for Clustering by the SOM	59
	Batch Self-Organizing Map	59
	Procedures of Clustering for Classification of Pressure Fluctuation Data and Operational Conditions	60
	Results and Discussions	61
	Simulations of Clustering of Operational Conditions	61
	Prediction of Dynamic Behavior of Interface Based on Clustering Map	64
	Conclusions	65
	References	66
	Intelligent Fuzzy Reasoning for Flood Risk Estimation in River Evros	67
	Introduction	67
	Necessity for a New Approach	68
	Necessity for Applying Flexible Models	69
	The Fuzzy Algebra Model	70
	Implementation of the IS	73
	Application in the Case of the Flood Risk Problem	73
	Discussion – Comparison to Existing Approaches	75
	References	77
	Fuzzy Logic and Artificial Neural Networks for Advanced Authentication Using Soft Biometric Data	79
	Introduction	79
	System Architecture	80
	Audio Hard Feature Extraction and Authentication	81
	Fingerprint Hard Feature Extraction and Authentication	83
	Soft-Biometric Feature Extraction	84
	Artificial Neural Network-Based Soft-Biometric Feature Scoring	85
	Fuzzy Logic-Based Fusion and Authentication	85
	Performance Evaluation	88
	Embedded Implementation	88
	Conclusions	89
	References	89
	Study of Alpha Peak Fitting by Techniques Based on Neural Networks	91
	Introduction	91
	Existing Solutions	92
	Proposed Solution	93
	Method	93
	Training Data	93
	Network Design	94
	Inputs and Output	95
	Results	96
	Conclusions	97
	References	97