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Descripción
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| This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neu- ral Networks that are able to approximate any function or classify any pattern set provided numeric information is injected into the net. Neural Nets usually have a supervised or unsupervised learning stage in order to perform desired response. Concerning symbolic information new research area has been developed, inspired by George Paun, called Membrane Sys- tems. A step forward, in a similar Neural Network architecture, was done to obtain Networks of Evolutionary Processors (NEP). A NEP is a set of processors connected by a graph, each processor only deals with sym- bolic information using rules. In short, objects in processors can evolve and pass through processors until a stable con guration is reach. This paper shows some ideas about these two models and how to incorpo- rate a learning stage, based on self-organizing algorithms, in networks of evolutionary processors. | |
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Internacional
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JCR del ISI
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Título de la revista
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IJDEM International Journal on Data Engineering and Management |
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ISSN
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1947-8534 |
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Factor de impacto JCR
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0 |
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Información de impacto
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Volumen
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DOI
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Número de revista
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Desde la página
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34 |
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Hasta la página
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41 |
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Mes
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ENERO |
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Ranking
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