Abstract: This paper presents some connectionist models that are widely used to solve NP-problems. Most well known numeric models are Neural 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 Systems. 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 symbolic information using rules. In short, objects in processors can evolve and pass through processors until a stable configuration is reach. This paper just shows some ideas about these two models.

Keywords: Natural Computation, Membrane Systems, Neural Networks, Networks of Evolutionary Processors.

ACM Classification Keywords: F.1.1 Models of Computation: Self-modifying machines (neural networks); F.4.1 Mathematical Logic: Computational logic

Link:

FILTERED NETWORKS OF EVOLUTIONARY PROCESSORS*

Luis Fernando de Mingo López, Eugenio Santos Menéndez,Francisco Gisbert

http://www.foibg.com/ijita/vol12/ijita12-1-p01.pdf