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ITHEA Classification Structure > F. Theory of Computation  > F.1 COMPUTATION BY ABSTRACT DEVICES  > F.1.1 Models of Computation 
GENERALIZING OF NEURAL NETS: FUNCTIONAL NETS OF SPECIAL TYPE
By: Donchenko et al. (3859 reads)
Rating: (1.00/10)

Abstract: Special generalizing for the artificial neural nets: so called RFT – FN – is under discussion in the report. Such refinement touch upon the constituent elements for the conception of artificial neural network, namely, the choice of main primary functional elements in the net, the way to connect them(topology) and the structure of the net as a whole. As to the last, the structure of the functional net proposed is determined dynamically just in the constructing the net by itself by the special recurrent procedure. The number of newly joining primary functional elements, the topology of its connecting and tuning of the primary elements is the content of the each recurrent step. The procedure is terminated under fulfilling “natural” criteria relating residuals for example. The functional proposed can be used in solving the approximation problem for the functions, represented by its observations, for classifying and clustering, pattern recognition, etc. Recurrent procedure provide for the versatile optimizing possibilities: as on the each step of the procedure and wholly: by the choice of the newly joining elements, topology, by the affine transformations if input and intermediate coordinate as well as by its nonlinear coordinate wise transformations. All considerations are essentially based, constructively and evidently represented by the means of the Generalized Inverse.

Keywords: Artificial neural network, approximating problem, beam dynamics with delay, optimization.

ACM Classification keywords:F.1.1.Models of Computation: Self modifying machines(neural networks; G.1.6. Optimization; H.1.m. Models and principles; I.2.6. Artificial Intelligence: learning, connectionism and neural nets.

Link:

GENERALIZING OF NEURAL NETS: FUNCTIONAL NETS OF SPECIAL TYPE

Volodymyr Donchenko, Mykola Kirichenko, Yuriy Krivonos

http://www.foibg.com/ijita/vol14/ijita14-3-p10.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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