
RKHSMETHODS AT SERIES SUMMATION FOR SOFTWARE IMPLEMENTATION
By: Svetlana Chumachenko, Ludmila Kirichenko
(3005 reads)
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Abstract: Reproducing Kernel Hilbert Space (RKHS) and Reproducing Transformation Methods for Series
Summation that allow analytically obtaining alternative representations for series in the finite form are developed.
Keywords: The reproducing transformation method, Hilbert space, reproducing kernel, RKHS, Series Summation
Method.
ACM Classification Keywords: G.1.10 Mathematics of Computing: Applications
Link:
RKHSMETHODS AT SERIES SUMMATION FOR SOFTWARE IMPLEMENTATION
Svetlana Chumachenko, Ludmila Kirichenko
http://www.foibg.com/ijita/vol13/ijita133p05.pdf

FINDING THE RELATIONSHIP BETWEEN A SEARCH ALGORITHM AND ...
By: Victor Nedel’ko, Svetlana Nedel’ko
(3444 reads)
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Abstract: The task of revealing the relationship between a search algorithm and a class of functions those it
solves is considered. Particularly, there was found a class of functions solvable by some adaptive search
algorithm for a discrete space of low cardinality. To find an optimal algorithm exhaustive search was used.
Algorithm quality criterion based on equivalence classes was also introduced.
Keywords: search algorithm, adaptive search, optimization, global extreme, no free launch theorem.
ACM Classification Keywords: G.1.6. Numerical analysis: Optimization; G.2.m. Discrete mathematics:
miscellaneous.
Link:
FINDING THE RELATIONSHIP BETWEEN A SEARCH ALGORITHM AND A CLASS OF FUNCTIONS ON DISCRETE SPACE BY EXHAUSTIVE SEARCH1
Victor Nedel’ko, Svetlana Nedel’ko
http://www.foibg.com/ijita/vol14/ijita144p06.pdf

DATA FLOW ANALYSIS AND THE LINEAR PROGRAMMING MODEL1
By: Levon Aslanyan
(3562 reads)
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Abstract: The general discussion of the data flow algorithmic models, and the linear programming problem with
the variating by data flow criterion function coefficients are presented. The general problem is widely known in
different names  data streams, incremental and online algorithms, etc. The more studied algorithmic models
include mathematical statistics and clustering, histograms and wavelets, sorting, set cover, and others. Linear
programming model is an addition to this list. Large theoretical knowledge exists in this as the simplex algorithm
and as interior point methods but the flow analysis requires another interpretation of optimal plans and plan
transition with variate coefficients. An approximate model is devised which predicts the boundary stability point for
the current optimal plan. This is valuable preparatory information of applications, moreover when a parallel
computational facility is supposed.
Keywords: data flow algorithm, linear programming, approximation
ACM Classification Keywords: G.1.6 Numerical analysis: Optimization
Link:
DATA FLOW ANALYSIS AND THE LINEAR PROGRAMMING MODEL1
Levon Aslanyan
http://www.foibg.com/ijita/vol13/ijita131p08.pdf

REPRESENTATION OF NEURAL NETWORKS BY DYNAMICAL SYSTEMS
By: Volodymyr Donchenko, Denys Serbaev
(3347 reads)
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Abstract: Representation of neural networks by dynamical systems is considered. The method of training of
neural networks with the help of the theory of optimal control is offered.
Keywords: neural nets, dynamical systems, training.
ACM Classification Keywords: G.1.6 Optimization, G.1.2 Approximation, I.2 Artificial Intelligence
Link:
REPRESENTATION OF NEURAL NETWORKS BY DYNAMICAL SYSTEMS
Volodymyr Donchenko, Denys Serbaev
http://www.foibg.com/ijita/vol12/ijita124p09.pdf

AN ALGORITHM FOR FRESNEL DIFFRACTION COMPUTING BASED ON FRACTIONAL ...
By: Georgi Stoilov
(3234 reads)
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Abstract: The fractional Fourier transform (FrFT) is used for the solution of the diffraction integral in optics. A
scanning approach is proposed for finding the optimal FrFT order. In this way, the process of diffraction
computing is speeded up. The basic algorithm and the intermediate results at each stage are demonstrated.
Key words: Fresnel diffraction, fractional Fouriertransform
ACM Classification Keywords: G.1.2 Fast Fourier transforms (FFT)
Link:
AN ALGORITHM FOR FRESNEL DIFFRACTION COMPUTING BASED ON FRACTIONAL FOURIER TRANSFORM
Georgi Stoilov
http://www.foibg.com/ijitk/ijitkvol01/ijitk012p14.pdf

DYNAMICAL SYSTEMS IN DESCRIPTION OF NONLINEAR RECURSIVE ...
By: Kirichenko et al.
(3145 reads)
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Abstract: The task of approximationforecasting for a function, represented by empirical data was investigated.
Certain class of the functions as forecasting tools: so called RFTtransformers, – was proposed. Least Square
Method and superposition are the principal composing means for the function generating. Besides, the special
classes of beam dynamics with delay were introduced and investigated to get classical results regarding
gradients. These results were applied to optimize the RFTtransformers. The effectiveness of the forecast was
demonstrated on the empirical data from the Forex market.
Keywords: empirical functions, learning samples, beam dynamics with delay, recursive nonlinear regressive
transformer, Generalized Inverse, Least Square Method.
ACM Classification Keywords: G.1.2 Approximation, G.1.3 Numerical Linear Algebra, G.1.6 Optimization
Link:
DYNAMICAL SYSTEMS IN DESCRIPTION OF NONLINEAR RECURSIVE REGRESSION TRANSFORMERS
Mykola Kirichenko, Volodymyr Donchenko, Denys Serbaev
http://www.foibg.com/ijita/vol13/ijita131p07.pdf

USING PHYSICAL QUANTITIES IN APPLIED MATHEMATICAL PROBLEMS WITH MAPLE
By: Tsvetanka Kovacheva
(3296 reads)
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Abstract. The present article discusses units of measure and their base units, work environments built in the
Units package of the computer algebra system Maple. An analysis is drawn of the tools of the application in
connection with the use of physical quantities and their features. Maple’s main commands are arranged in groups
depending on the function. Some applied mathematical problems are given as examples making use of
derivative, integral and differential equations.
Key words: conversion, dimension, Maple, unit, systems of quantities, systems of units, Units package.
ACM Classification Keywords: G. Mathematics of Computing, I. Computing Methodologies
Link:
USING PHYSICAL QUANTITIES IN APPLIED MATHEMATICAL PROBLEMS WITH MAPLE
Tsvetanka Kovacheva
http://www.foibg.com/ijitk/ijitkvol02/ijitk025p08.pdf

CONTRADICTION VERSUS SELFCONTRADICTION IN FUZZY LOGIC*
By: Torres et al.
(2990 reads)
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Abstract: Trillas et al. introduced in 7 and 8 the concepts of both selfcontradictory fuzzy set and contradiction
between two fuzzy sets. Later, in 1 and 2 the necessity of determine not only the contradiction, but also the
degree in that this property occurs, was considered. This paper takes up again these subjects, and firstly we
study if there exists some connection between the two first notions. After that, taking into account that selfcontradiction
of a fuzzy set could be understood as the contradiction with itself, and starting from the degrees of
contradiction between two fuzzy sets proposed in 5, we obtain degrees of selfcontradiction. Finally,
preservation of some intuitive properties both in the use of connectives and in the obtaining of new knowledge
throughout compositional rule of inference, are tested.
Keywords: fuzzy sets, tnorm, tconorm, strong fuzzy negations, contradiction, measures of contradiction, fuzzy
relation, compositional rule of inference.
ACM Classification Keywords: F.4.1 Mathematical Logic and Formal Languages: Mathematical Logic (Model
theory, Set theory); I.2.3 Artificial Intelligence: Deduction and Theorem Proving (Uncertainty, “fuzzy” and
probabilistic reasoning); I.2.4 Artificial Intelligence: Knowledge Representation Formalisms and Methods
(Predicate logic, Representation languages).
Link:
CONTRADICTION VERSUS SELFCONTRADICTION IN FUZZY LOGIC*
Carmen Torres, Susana Cubillo, Elena Castineira
http://www.foibg.com/ijita/vol14/ijita144p05.pdf

A GEOMETRICAL INTERPRETATION TO DEFINE CONTRADICTION
By: Torres et al.
(3061 reads)
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Abstract: For inference purposes in both classical and fuzzy logic, neither the information itself should be
contradictory, nor should any of the items of available information contradict each other. In order to avoid these
troubles in fuzzy logic, a study about contradiction was initiated by Trillas et al. in 5 and 6. They introduced the
concepts of both selfcontradictory fuzzy set and contradiction between two fuzzy sets. Moreover, the need to
study not only contradiction but also the degree of such contradiction is pointed out in 1 and 2, suggesting
some measures for this purpose. Nevertheless, contradiction could have been measured in some other way. This
paper focuses on the study of contradiction between two fuzzy sets dealing with the problem from a geometrical
point of view that allow us to find out new ways to measure the contradiction degree. To do this, the two fuzzy
sets are interpreted as a subset of the unit square, and the so called contradiction region is determined. Specially
we tackle the case in which both sets represent a curve in 0,12. This new geometrical approach allows us to
obtain different functions to measure contradiction throughout distances. Moreover, some properties of these
contradiction measure functions are established and, in some particular case, the relations among these different
functions are obtained.
Keywords: fuzzy sets, tnorm, tconorm, fuzzy strong negations, contradiction, measures of contradiction.
ACM Classification Keywords: F.4.1 Mathematical Logic and Formal Languages: Mathematical Logic (Model
theory, Set theory); I.2.3 Artificial Intelligence: Deduction and Theorem Proving (Uncertainty, “fuzzy” and
probabilistic reasoning); I.2.4 Artificial Intelligence: Knowledge Representation Formalisms and Methods
(Predicate logic, Representation languages).
Link:
A GEOMETRICAL INTERPRETATION TO DEFINE CONTRADICTION DEGREES BETWEEN TWO FUZZY SETS
Carmen Torres, Elena Castiñeira, Susana Cubillo, Victoria Zarzosa
http://www.foibg.com/ijita/vol12/ijita122p04.pdf

REALIZATION OF AN OPTIMAL SCHEDULE FOR THE TWOMACHINE
By: Natalja Leshchenko, Yuri Sotskov
(3547 reads)
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Abstract: Nonpreemptive twomachine flowshop scheduling problem with uncertain processing times of n jobs
is studied. In an uncertain version of a scheduling problem, there may not exist a unique schedule that remains
optimal for all possible realizations of the job processing times. We find necessary and sufficient conditions
(Theorem 1) when there exists a dominant permutation that is optimal for all possible realizations of the job
processing times. Our computational studies show the percentage of the problems solvable under these
conditions for the cases of randomly generated instances with n ≤100 . We also show how to use additional
information about the processing times of the completed jobs during optimal realization of a schedule (Theorems
2 – 4). Computational studies for randomly generated instances with n ≤ 50 show the percentage of the twomachine
flowshop scheduling problems solvable under the sufficient conditions given in Theorems 2 – 4.
Keywords: Scheduling, flowshop, makespan, uncertainty.
ACM Classification Keywords: F.2.2 Nonnumerical algorithms and problems: Sequencing and scheduling
Link:
REALIZATION OF AN OPTIMAL SCHEDULE FOR THE TWOMACHINE FLOWSHOP WITH INTERVAL JOB PROCESSING TIMES
Natalja Leshchenko, Yuri Sotskov
http://www.foibg.com/ijita/vol14/ijita142p12.pdf

RECENT RESULTS ON STABILITY ANALYSIS ...
By: Yuri Sotskov
(3428 reads)
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(1.00/10)

Abstract: Two assembly line balancing problems are addressed. The first problem (called SALBP1) is to
minimize number of linearly ordered stations for processing n partially ordered operations V = {1, 2, ..., n} within
the fixed cycle time c. The second problem (called SALBP2) is to minimize cycle time for processing partially
ordered operations V on the fixed set of m linearly ordered stations. The processing time ti of each operation
i ∈V is known before solving problems SALBP1 and SALBP2. However, during the life cycle of the assembly
line the values ti are definitely fixed only for the subset of automated operations V\V~ . Another subset V~ ⊆V
includes manual operations, for which it is impossible to fix exact processing times during the whole life cycle of
the assembly line. If j ∈V~, then operation times tj can differ for different cycles of the production process. For the
optimal line balance b of the assembly line with operation times t1, t2, …, tn, we investigate stability of its optimality
with respect to possible variations of the processing times tj of the manual operations j ∈V~.
Keywords: Scheduling, robustness and sensitivity analysis, assembly line.
ACM Classification Keywords: F.2.2 Nonnumerical algorithms and problems: Sequencing and scheduling.
Link:
RECENT RESULTS ON STABILITY ANALYSIS OF AN OPTIMAL ASSEMBLY LINE BALANCE
Yuri Sotskov
http://www.foibg.com/ijita/vol14/ijita142p11.pdf

LEARNING TECHNOLOGY IN SCHEDULING BASED ON THE MIXED GRAPHS
By: Sotskov et al.
(3109 reads)
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(1.00/10)

Abstract: We propose the adaptive algorithm for solving a set of similar scheduling problems using learning
technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and
heuristics oriented on the realworld scheduling problems. The former may ensure high quality of the solution by
means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain
type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in
performance measure) and efficient (in computational time) heuristics by adapting local decisions for the
scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of
sample scheduling problems using a branchandbound algorithm and structuring knowledge using pattern
recognition apparatus.
Keywords: Scheduling, mixed graph, learning, pattern recognition.
ACM Classification Keywords: F.2.2 Nonnumerical algorithms and problems: sequencing and scheduling.
Link:
LEARNING TECHNOLOGY IN SCHEDULING BASED ON THE MIXED GRAPHS
Yuri Sotskov, Nadezhda Sotskova, Leonid Rudoi
http://www.foibg.com/ijita/vol12/ijita124p12.pdf

SELECTING CLASSIFIERS TECHNIQUES FOR OUTCOME PREDICTION ...
By: Tatiana Shatovskaya
(2979 reads)
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(1.00/10)

Abstract: This paper presents an analysis of different techniques that is designed to aid a researcher in
determining which of the classification techniques would be most appropriate to choose the ridge, robust and
linear regression methods for predicting outcomes for specific quasistationary process.
Keywords: classification techniques, neural network, composite classifier
ACM Classification Keywords: F.2.1 Numerical Algorithms and Problems
Link:
SELECTING CLASSIFIERS TECHNIQUES FOR OUTCOME PREDICTION USING NEURAL NETWORKS APPROACH
Tatiana Shatovskaya
http://www.foibg.com/ijita/vol14/ijita143p15.pdf

MATRICIAL MODEL FOR THE STUDY OF LOWER BOUNDS
By: Jose Joaquin Erviti, Adriana Toni
(2936 reads)
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(1.00/10)

Abstract: Let V be an array. The range query problem concerns the design of data structures for implementing
the following operations. The operation update(j,x) has the effect v v x j j ← + , and the query operation
retrieve(i,j) returns the partial sum i j v +K+ v . These tasks are to be performed online. We define an
algebraic model – based on the use of matrices – for the study of the problem. In this paper we establish as well
a lower bound for the sum of the average complexity of both kinds of operations, and demonstrate that this lower
bound is near optimal – in terms of asymptotic complexity.
Keywords: zeroone matrices, lower bounds, matrix equations
ACM Classification Keywords: F.2.1 Numerical Algorithms and Problems
Link:
MATRICIAL MODEL FOR THE STUDY OF LOWER BOUNDS
Jose Joaquin Erviti, Adriana Toni
http://www.foibg.com/ijita/vol13/ijita131p09.pdf

OPTIMAL CONTROL OF A SECOND ORDER PARABOLIC HEAT EQUATION
By: Mahmoud Farag, Mainouna AlManthari
(3017 reads)
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(1.00/10)

Abstract: In this paper, we are concerned with the optimal control boundary control of a second order parabolic
heat equation. Using the results in Evtushenko, 1997 and spatial central finite difference with diagonally implicit
RungeKutta? method (DIRK) is applied to solve the parabolic heat equation. The conjugate gradient method
(CGM) is applied to solve the distributed control problem. Numerical results are reported.
Keywords: Distributed control problems, Second order parabolic heat equation, RungeKutta? method, CGM.
ACM Classification Keywords: F.2.1 Numerical Algorithms and Problems; G.4 Mathematical Software
Link:
OPTIMAL CONTROL OF A SECOND ORDER PARABOLIC HEAT EQUATION
Mahmoud Farag, Mainouna AlManthari?
http://www.foibg.com/ijita/vol13/ijita133p03.pdf

THE NEW SOFTWARE PACKAGE FOR DYNAMIC HIERARCHICAL CLUSTERING ...
By: Shatovska et al.
(3081 reads)
Rating:
(1.00/10)

Abstract: In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in
large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of
methods for clustering complex shapes and types of data, highdimensional clustering techniques, and methods
for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach
based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified
hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested
approach will be demonstrated by the experimental results.
Keywords: Chameleon, clustering, hypergraph partitioning, coarsening hypergraph.
ACM Classification Keywords F.2.1 Numerical Algorithms and Problems
Link:
THE NEW SOFTWARE PACKAGE FOR DYNAMIC HIERARCHICAL CLUSTERING FOR CIRCLES TYPES OF SHAPES
Tetyana Shatovska, Tetiana Safonova, Iurii Tarasov
http://www.foibg.com/ijita/vol15/ijita152p13.pdf

WEBCOMPUTING SERVICE FRAMEWORK
By: Evgenija Popova
(3369 reads)
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(1.00/10)

Abstract: Presented is webComputing – a general framework of mathematically oriented services including
remote access to hardware and software resources for mathematical computations, and web interface to dynamic
interactive computations and visualization in a diversity of contexts: mathematical research and engineering,
computeraided mathematical/technical education and distance learning. webComputing builds on the innovative
webMathematica technology connecting technical computing system Mathematica to a web server and providing
tools for building dynamic and interactive webinterface to Mathematicabased functionality. Discussed are the
conception and some of the major components of webComputing service: Scientific Visualization, Domain
Specific Computations, Interactive Education, and Authoring of Interactive Pages.
Keywords: webaccess, mathematical userinterfaces, web computations, mathematical active learning.
ACM Classification Keywords: F.1.2 Modes of Computation: interactive computation, online computation; G.4
Mathematical Software: user interfaces; K.3.1 Computer Use in Education: distance learning.
Link:
WEBCOMPUTING SERVICE FRAMEWORK
Evgenija Popova
http://www.foibg.com/ijita/vol13/ijita133p08.pdf

NETWORKS OF EVOLUTIONARY PROCESSORS: JAVA IMPLEMENTATION ...
By: Díaz et al.
(3055 reads)
Rating:
(1.00/10)

Abstract: This paper is focused on a parallel JAVA implementation of a processor defined in a Network of
Evolutionary Processors. Processor description is based on JDom, which provides a complete, Javabased
solution for accessing, manipulating, and outputting XML data from Java code. Communication among different
processor to obtain a fully functional simulation of a Network of Evolutionary Processors will be treated in future.
A safethread model of processors performs all parallel operations such as rules and filters. A nondeterministic
behavior of processors is achieved with a thread for each rule and for each filter (input and output). Different
results of a processor evolution are shown.
Keywords: Networks of Evolutionary Processors, Membrane Systems, Natural Computation.
ACM Classification Keywords: F.1.2 Modes of Computation, I.6.1 Simulation Theory, H.1.1 Systems and Information Theory
Link:
NETWORKS OF EVOLUTIONARY PROCESSORS: JAVA IMPLEMENTATION OF A THREADED PROCESSOR1
Miguel Angel Díaz, Luis Fernando de Mingo López, Nuria Gómez Blas
http://www.foibg.com/ijita/vol15/ijita151p06.pdf

NETWORKS OF EVOLUTIONARY PROCESSORS (NEP) AS DECISION SUPPORT SYSTEMS1
By: Blas et al.
(3112 reads)
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(1.00/10)

Abstract: This paper presents the application of Networks of Evolutionary Processors to Decision Support
Systems, precisely KnowledgeDriven? DSS. Symbolic information and rulebased behavior in Networks of
Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network.
The nondeterministic and massive parallel way of operation results in NPproblem solving in linear time.
A working NEP example is shown.
Keywords: Natural Computing, Networks of Evolutionary Processors, Decision Support Systems.
ACM Classification Keywords: F.1.2 Modes of Computation, I.6.1 Simulation Theory, H.1.1 Systems and Information Theory.
Link:
NETWORKS OF EVOLUTIONARY PROCESSORS (NEP) AS DECISION SUPPORT SYSTEMS1
Nuria Gómez Blas, Miguel Angel Díaz, Juan Castellanos, Francisco Serradilla
http://www.foibg.com/ijita/vol15/ijita151p05.pdf

STATIC ANALYSIS OF USEFULNESS STATES IN TRANSITION P SYSTEMS
By: Frutos et al.
(3103 reads)
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(1.00/10)

Abstract: Transition P Systems are a parallel and distributed computational model based on the notion of the
cellular membrane structure. Each membrane determines a region that encloses a multiset of objects and
evolution rules. Transition P Systems evolve through transitions between two consecutive configurations that are
determined by the membrane structure and multisets present inside membranes. Moreover, transitions between
two consecutive configurations are provided by an exhaustive nondeterministic and parallel application of
evolution rules. But, to establish the rules to be applied, it is required the previous calculation of useful, applicable
and active rules. Hence, computation of useful evolution rules is critical for the whole evolution process efficiency,
because it is performed in parallel inside each membrane in every evolution step. This work defines usefulness
states through an exhaustive analysis of the P system for every membrane and for every possible configuration of
the membrane structure during the computation. Moreover, this analysis can be done in a static way; therefore
membranes only have to check their usefulness states to obtain their set of useful rules during execution.
Keywords: Evolution Rules, Usefulness States, Transition P System, Sequential Machines, Static Analysis
ACM Classification Keywords: F.1.1 Computation by abstract devices – Models of computation. D.1.m
Miscellaneous – Natural Computing
Link:
STATIC ANALYSIS OF USEFULNESS STATES IN TRANSITION P SYSTEMS
Juan Alberto Frutos, Luis Fernandez, Fernando Arroyo, Gines Bravo
http://www.foibg.com/ijitk/ijitkvol02/ijitk021p10.pdf

GENERALIZING OF NEURAL NETS: FUNCTIONAL NETS OF SPECIAL TYPE
By: Donchenko et al.
(3015 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/ijita143p10.pdf

AUTOMATA–BASED METHOD FOR SOLVING SYSTEMS OF LINEAR CONSTRAINTS IN {0,1}
By: Krivoi et al.
(3131 reads)
Rating:
(1.00/10)

Abstract: We consider a finite state automata based method of solving a system of linear Diophantine equations
with coefficients from the set {1,0,1} and solutions in {0,1}.
Keywords: system of the linear Diophantine equations, set of the basis solutions, outputless finite state
automaton.
ACM Classification Keywords: F.1.1 Models of Computation  finite automata, systems of linear Diophantine constraints
Link:
AUTOMATA–BASED METHOD FOR SOLVING SYSTEMS OF LINEAR CONSTRAINTS IN {0,1}
Sergey Krivoi, Lyudmila Matvyeyeva, Wioletta Grzywacz
http://www.foibg.com/ijita/vol12/ijita124p10.pdf

FILTERED NETWORKS OF EVOLUTIONARY PROCESSORS*
By: López et al.
(3289 reads)
Rating:
(1.00/10)

Abstract: This paper presents some connectionist models that are widely used to solve NPproblems. 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: Selfmodifying 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/ijita121p01.pdf

NEURAL CONTROL OF CHAOS AND APLICATIONS
By: Hernández et al.
(3054 reads)
Rating:
(1.00/10)

Abstract: Signal processing is an important topic in technological research today. In the areas of nonlinear
dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over
the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has
led to widespread use of such networks to control dynamic systems learning. This paper presents
backpropagationbased neural network architecture that can be used as a controller to stabilize unsteady periodic
orbits. It also presents a neural networkbased method for transferring the dynamics among attractors, leading to
more efficient system control. The procedure can be applied to every point of the basin, no matter how far away
from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a
backpropagation neural network as a filter to separate and control both signals at the same time. The neural
network provides more effective control, overcoming the problems that arise with control feedback methods.
Control is more effective because it can be applied to the system at any point, even if it is moving away from the
target state, which prevents waiting times. Also control can be applied even if there is little information about the
system and remains stable longer even in the presence of random dynamic noise.
Keywords: Neural Network, Backpropagation, Chaotic Dynamic Systems, Control Feedback Methods.
ACM Classification Keywords: F.1.1 Models of Computation: Selfmodifying machines (neural networks);
F.1.2 Modes of Computation: Alternation and nondeterminism; G.1.7 Ordinary Differential Equations: Chaotic
systems; G.3 Probability and Statistics: Stochastic processes
Link:
NEURAL CONTROL OF CHAOS AND APLICATIONS
Cristina Hernández, Juan Castellanos, Rafael Gonzalo, Valentín Palencia
http://www.foibg.com/ijita/vol12/ijita122p01.pdf

SOLVING A DIRECT MARKETING PROBLEM BY THREE TYPES OF ARTMAP NEURAL NETWORKS
By: Anatoli Nachev
(3073 reads)
Rating:
(1.00/10)

Abstract: An important task for a direct mailing company is to detect potential customers in order to avoid
unnecessary and unwanted mailing. This paper describes a nonlinear method to predict profiles of potential
customers using dARTMAP, ARTMAPIC, and Fuzzy ARTMAP neural networks. The paper discusses
advantages of the proposed approaches over similar techniques based on MLP neural networks.
Keywords: ARTMAP, neural networks, data mining
ACM Classification Keywords: F.1.1 Models of Computation  neural networks, H.2.8 Database Applications data mining.
Link:
SOLVING A DIRECT MARKETING PROBLEM BY THREE TYPES OF ARTMAP NEURAL NETWORKS
Anatoli Nachev
http://www.foibg.com/ijita/vol15/ijita151p10.pdf


