Soft Computing Methods for Practical Environment Solutions: Techniques and Studies: Techniques and Studies

Front Cover
Gestal Pose, Marcos, Rivero Cebri n, Daniel
IGI Global, May 31, 2010 - Computers - 452 pages

Nature provides inspiration and guidance in the creation of techniques, applications and new technologies in the fields of Artificial Intelligence and Soft Computing.

Soft Computing Methods for Practical Environment Solutions: Techniques and Studies presents various practical applications of Soft Computing techniques in real-world situations and problems, aiming to show the enormous potential of such techniques in solving all kinds of problems, and thus, providing the latest advances in these techniques in an extensive state-of-the-art and a vast theoretical study. Ideal for students studying AI and researchers familiarizing themselves with such techniques, so to offer recent and novel applications, helping expand and explore new areas of research.

 

Contents

Artificial Neural Networks andEvolutionary Computation
1
Artificial Cell Model Used forInformation Processing
12
Soft Computing Techniques forHumanComputer Interaction
30
LVQ Neural Networks inColor Segmentation
45
A Descriptive Overview
64
User Modeling in SoftComputing Framework
75
Electromagnetic OptimizationUsing Genetic Algorithms
93
A PROMETHEE and TrigonometricDifferential Evolution Analysisof their Chemical Emissions
106
GABased Data Mining Appliedto Genetic Data for theDiagnosis of Complex Diseases
219
Improving Ontology Alignmentthrough Genetic Algorithms
240
Characterization andModelization of SurfaceNet Radiation throughNeural Networks
260
Application of Machine LearningTechniques in the Study of theRelevance of EnvironmentalFactors in Prediction ofTropospheric Ozone
278
Evolutionary LagrangianInverse Modeling for PM10Pollutant Dispersion
293
Artificial Intelligence Applied toNatural Resources Management
313
Applications of SelfOrganizing Maps to AddressEnvironmental Studies
331
Neural Models forRainfall Forecasting
353

A Soft Computing System forModelling the Manufactureof Steel Components
127
Time Series Prediction
143
Intrinsic EvolvableHardware Structures
160
Connectionist Systems andSignal Processing TechniquesApplied to the Parameterizationof Stellar Spectra
187
Automatic Arrhythmia Detection
204
Compilation of References
370
About the Contributors
404
Index
420
Copyright

Other editions - View all

Common terms and phrases

About the author (2010)

Marcos Gestal is an assistant professor in the Computer Science Faculty (University of A Coru¤a) and member of the research laboratory Artificial Neural Networks and Adaptative Systems. He has obtained his PhD degree in Computer Science in 2007, with a thesis about multimodal problem resolution using several approaches based on Genetic Algorithms. His actual research interests are focused on evolutionary computation (mainly genetic algorithms), artificial neural networks and their interaction to perform variable selection. Also he is interested in security task, so he is actually teaching in the information systems security subject. He has participated in several research projects and published papers in many international journals and books.

Daniel Rivero was born in A Coru¤a on the January, 30th 1978. He has obtained his MS degree in Computer Science by the University of A Coru¤a, A Coru¤a, Spain in 2001 and the PhD degree in Computer Science in 2007 by the same university. He is currently an assistant professor at the Faculty of Computer Science of the University of A Coru¤a. Before he gets that academic role, he has received different research grants from different administrations for more than four years. His main research interests are Artificial Neural Networks, Genetic Algorithms, Genetic Programming and Adaptative Systems. [Editor]

Bibliographic information