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ITHEA Classification Structure > F. Theory of Computation  > F.1 COMPUTATION BY ABSTRACT DEVICES  > F.1.1 Models of Computation 
FILTERED NETWORKS OF EVOLUTIONARY PROCESSORS*
By: López et al. (4136 reads)
Rating: (1.00/10)

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

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F.1.1 Models of Computation
article: ALGORITHMIZATION PROCESS FOR FRACTAL ANALYSIS IN THE CHAOTIC DYNAMICS OF ... · INTELLIGENT TRADING SYSTEMS · AN ARCHITECTURE FOR REPRESENTING BIOLOGICAL PROCESSES BASED ON NETWORKS... · Polynomial Regression using a Perceptron with Axo-axonic Connections · ACCOUNTING IN THEORETICAL GENETICS · A NEW METHOD FOR THE BINARY ENCODING AND HARDWARE IMPLEMENTATION OF METABOLIC P · GENETIC BASED SPOT DETECTION METHOD IN TWO-DIMENSIONAL ELECTROPHORESIS IMAGES · Self-Organizing Architectural design based on Morphogenetic Programming · PRION CRYSTALIZATION MODEL AND ITS APPLICATION TO RECOGNITION PATTERN · POLYNOMIAL APPROXIMATION USING PARTICLE SWARM OPTIMIZATION OF LINEAR ... · MULTIPLE-MODEL DESCRIPTION AND STRUCTURE DYNAMICS ANALYSIS OF ACTIVE MOVING... · COMPUTATIONAL MODEL FOR SERENDIPITY · STRING MEASURE APPLIED TO STRING SELF-ORGANIZING MAPS AND NETWORKS OF ... · CLASSIFICATION OF DATA TO EXTRACT KNOWLEDGE FROM NEURAL NETWORKS · SIMULTANEOUS CONTROL OF CHAOTIC SYSTEMS USING RBF NETWORKS · TIMED TRANSITION AUTOMATA AS NUMERICAL PLANNING DOMAIN · STATIC ANALYSIS OF USEFULNESS STATES IN TRANSITION P SYSTEMS · GENERALIZING OF NEURAL NETS: FUNCTIONAL NETS OF SPECIAL TYPE · AUTOMATA–BASED METHOD FOR SOLVING SYSTEMS OF LINEAR CONSTRAINTS IN {0,1} · FILTERED NETWORKS OF EVOLUTIONARY PROCESSORS* · NEURAL CONTROL OF CHAOS AND APLICATIONS · SOLVING A DIRECT MARKETING PROBLEM BY THREE TYPES OF ARTMAP NEURAL NETWORKS ·
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