Artificial Neural Networks in Real-Life Applications

Juan R. Rabunal, Julian Dorado

376 pages, parution le 17/03/2006

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Résumé

Artificial Neural Networks in Real-Life Applications offers an outlook on the most recent works in the field of artificial neural networks (ANN). It includes theoretical developments of the ANN area and applications of these systems, using intelligent characteristics for adaptability, automatic learning, classification, prediction and even artistic creation.

Artificial Neural Networks in Real-Life Applications is a summary of recent advances in the ANN area from a practical perspective. It shows studies of the applications in time series forecasting, extraction of knowledge, civil engineering, economical field, artistic creation (music), cost minimization, intruder detection, and many others, making it a very important source of ideas for research in this area.

Sommaire

Biological Modelization
- Neuroglial Behaviour in Computer Science
- Astrocytes and the Biological Neural Networks
Time Series Forecasting
- Time Series Forecasting by Evolutionary Neural Networks
- Development of ANN with Adaptive Connections by CE
Data Mining
- Self-Adapting Intelligent Neural Systems Using Evolutionary Techniques
- Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
- Several Approaches to Variable Selection by Means of Genetic Algorithms
Civil Engineering
- Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering
- Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks
Financial Analysis
- Soft Computing Approach for Bond Rating Prediction
- Predicting Credit Ratings with a GA-MLP Hybrid
Other Applications
- Music and Neural Networks
- Connectionist Systems for Fishing Prediction
- A Neural Network Approach to Cost Minimization in a Production Scheduling Setting
- Intrusion Detection Using Modern Techniques: Integration of Genetic Algorithms and Rough Sets with Neural Nets
- Cooperative Artificial Intelligence Techniques for Stellar Spectra Classification: A Hybrid Strategy

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Idea Group Publishing
Auteur(s)	Juan R. Rabunal, Julian Dorado
Parution	17/03/2006
Nb. de pages	376
Format	18 x 25,5
Couverture	Broché
Poids	680g
Intérieur	Noir et Blanc
EAN13	9781591409038
ISBN13	978-1-59140-903-8