Integrating Matlab Neural Networks Toolbox functionality in a fully reusable software component library

In this work, we present a reusable software component library, called IMO.Net Artificial Neural Networks library, which encapsulates the functionality of the Matlab Neural Networks Toolbox (MNNT). The MNNT is a powerful tool to work with neural networks. However, MNNT has not been conceived as a re...

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Bibliographic Details
Published in:Neural computing & applications 2007-05, Vol.16 (4-5), p.471-479
Main Authors: Méndez, Arturo J., Roselló, Emilio G., Lado, María J., Dacosta, Jacinto G., Cota, Manuel P.
Format: Article
Language:English
Subjects:
Applications programs
Artificial neural networks
Libraries
Matlab
Neural networks
Object-oriented programming
Rapid application development
Software
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Summary:In this work, we present a reusable software component library, called IMO.Net Artificial Neural Networks library, which encapsulates the functionality of the Matlab Neural Networks Toolbox (MNNT). The MNNT is a powerful tool to work with neural networks. However, MNNT has not been conceived as a reusable and integrable software piece, and its results are clearly inadequate to be used for development of applications. The component library presented in this paper is fully reusable, allowing the integration of the neural networks toolbox objects and functions in software applications, independently of the platform and tools used to build it. Furthermore, this library provides two different sets of classes to the programmers, one of them presenting an application program interface (API) similar to the Matlab toolbox, and the other a fully object-oriented designed API, which is easier to use and more adequate for object-oriented and rapid application development. This library combines the advantages of being fully reusable and internally employing the MNNT to perform the algorithmic task, consequently inheriting all its power, robustness and comprehensiveness, but without its reusability limitations.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-006-0075-5