ARTIFICIAL NEURAL NETWORKS IN REAL-LIFE APPLICATIONS

DETAIL

• Author : Juan R. Rabuñal and Julián Dorado
• Language : English
• Published : 2006
• Page : 395

BOOK ORGANIZATION

This book is organized into six sections with 16 chapters. A brief revision of each chapter is presented as follows:

Section I presents recent advances in the study of biological neurons and also shows how these advances can be used for developing new computational models of ANNs.

• Chapter I shows a study that incorporates, into the connectionist systems, new elements that emulate cells of the glial system. The proposed connectionist systems are known as artificial neuroglial networks (ANGN).
• Chapter II expands artificial neural networks to artificial neuroglial networks in which glial cells are considered. 

New techniques such as connectionist techniques are preferred in cases like the time series analysis, which has been an area of active investigation in statistics for a long time, but has not achieved the expected results in numerous occasions. Section II shows the application of ANNs to predict temporal series.

• Chapter III shows a hybrid evolutionary computation with artificial neural network combination for time series prediction. This strategy was evaluated with 10 time series and compared with other methods.
• Chapter IV presents the use of artificial neural networks and evolutionary techniques for time series forecasting with a multilevel system to adjust the ANN architecture.

In the world of databases the knowledge discovery (a technique known as data mining) has been a very useful tool for many different purposes and tried with many different techniques. Section III describes different ANNs-based strategies for knowledge search and its extraction from stored data.

• Chapter V describes genetic algorithm-based evolutionary techniques for automatically constructing intelligent neural systems. This system is applied in laboratory tests and to a real-world problem: breast cancer diagnosis.
• Chapter VI shows a technique that makes the extraction of the knowledge held by previously trained artificial neural networks possible. Special emphasis is placed on recurrent neural networks.
• Chapter VII shows several approaches in order to determine what should be the most relevant subset of variables for the performance of a classification task. The solution proposed is applied and tested on a practical case in the field of analytical chemistry, for the classification of apple beverages.

The advances in the field of artificial intelligence keep having strong influence over the area of civil engineering. New methods and algorithms are emerging that enable civil engineers to use computing in different ways. Section IV shows two applications of ANNs to this field. The first one is referred to the hydrology area and the second one to the building area.

• Chapter VIII describes the application of artificial neural networks and evolutionary computation for modeling the effect of rain on the run-off flow in a typical urban basin.
• Chapter IX makes predictions of the consistency of concrete by means of the use of artificial neuronal networks.

The applications at the economical field, mainly for prediction tasks, are obviously quite important, since financial analysis is one of the areas of research where new techniques, as connectionist systems, are continuously applied. Section V shows both applications of ANNs to predict tasks in this field; one of them is for bond-rating prediction, and the other for credit-rating prediction:

• Chapter X shows an application of soft computing techniques on a high dimensional problem: bond-rating prediction. Dimensionality reduction, variable reduction, hybrid networks, normal fuzzy, and ANN are applied in order to solve this problem.
• Chapter XI provides an example of how task elements for the construction of an ANN can be automated by means of an evolutionary algorithm, in a credit rating prediction.

Finally, section VI shows several applications of ANNs to really new areas, demonstrating the interest of different science investigators in facing real-world problems. As a small sample of the areas where ANNs are used, this section presents applications for music creation (Chapter XII), exploitation of fishery resources (Chapter XIII), cost minimisation in production schedule setting (Chapter XIV), techniques of intruder detection (Chapter XV), and an astronomy application for stellar images (Chapter XVI).

• Chapter XII explains the complex relationship between music and artificial neural networks, highlighting topics such as music composition or representation of musical language.
• Chapter XIII approaches the foundations of a new support system for fisheries, based on connectionist techniques, digital image treatment, and fuzzy logic. 
• Chapter XIV proposes an artificial neural network model for obtaining a control strategy. This strategy is expected to be comparable to the application of cost estimation and calculation methods.
• Chapter XV shows a novel hybrid method for the integration of rough set theory, genetic algorithms, and an artificial neural network. The goal is to develop an intrusion detection system.
• Finally, Chapter XVI describes a hybrid approach to the unattended classification of low-resolution optical spectra of stars by means of integrating several artificial intelligence techniques.

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