N |
Оглавление |
Страницы |
1 |
An overview of data mining
concepts based on neural networks |
3-17 |
2 |
Using of stochastic
approximation type algorithm for selection of optimal step-size of local
voting protocol for differentiated consensuses achievement in a multi-agent
network with topology cost constraints |
18-38 |
3 |
Metrics for heuristic algorithms
for solving the problem of automata minimization |
39-43 |
4 |
Heuristic algorithms for
generating binary relation matrices on the states of canonical automata |
44-55 |
5 |
Evaluation of possible ranges
for the output of linear systems subjected to exogenous disturbances |
56-64 |
An Overview of Data Mining Concepts Based on Neural
Networks
V. A. Erofeeva
St. Petersburg State University
v.erofeeva@spbu.ru
Key words: neural networks, pattern recognition,
supervised learning, chaotic neural networks, recurrent neural networks,
oscillatory neural networks, deep learning.
At present, the development of aspects of
human’s activity is associated with generation and accumulation of large
amounts of data, which may contain important practical information. In recent
years, methods of data mining automation based on intellectual data analysis
have been developing. Artificial neural networks, inspired by biological neural
networks, are used in many areas of information technology, including computer
vision, voice recognition, natural language processing. In this paper we
provide an analysis of existing neural network concepts, their classification
and consider their application to the problem of pattern recognition.
Bibliogr.: 50 refs.
Using of Stochastic Approximation Type Algorithm
for Selection of Optimal Step-size of Local Voting Protocol for Differentiated
Consensuses Achievement in a Multi-agent Network with Topology Cost Constraints
Y. V. Ivanskiy
St. Petersburg State University
ivanskiy.yuiry@gmail.com
Key words: differentiated consensuses, multi-agent
systems, randomized algorithms, stochastic approximation, SPSA.
In this paper, a new consensus problem,
differentiated consensuses, is studied. This consensus problem is that, in a
system with multiple classes, consensus is desired for every class, and the
values themselves may be different for different classes. Specifically, we
investigate differentiated consensuses in a distributed stochastic network
system of nodes (agents), where tasks, classified with different priorities,
are serviced. The network system is assumed to have a switched topology, noise,
and delays in measurements, and the cost on the topology.
The goal is to reach/maintain balanced
(equal) load, i.e., consensus, across the network and, at the same time, to
satisfy the topology cost constraint, both for every priority class. A control
protocol is proposed. With this protocol, network resources are allocated in a
randomized way with probabilities corresponding to each priority class.
Step-size of proposed protocol is selected using stochastic approximation type
algorithm. We prove that the proposed control protocol is able to meet the
topology cost constraint and achieve approximate consensus for each of the
priority classes in the network.
Bibliogr.: 54 refs.
Metrics for Heuristic Algorithms for Solving
the Problem of Automata Minimization1
E. A. Melnikova, M.
A. Trenina
Samara State University, Togliatti State
University
ya.e.melnikova@yandex.ru, trenina.m.a@yandex.ru
Key words: discrete optimization; heuristic
algorithms; clustering situations; minimization of nondeterministic finite
automata; metrics.
To minimize nondeterministic finite automata,
clustering algorithms are applied for the subtasks in a special
multi-heuristics approach to discrete optimization. Over the resulting set of subproblems, various discrete metrics are considered and
their properties are investigated.
Bibliogr.: 7 refs.
Heuristic Algorithms for Generating Binary
Relation Matrices on the States of Canonical Automata
N. V. Sofonova
Togliatti State Universit
natashamilkova@yandex.ru
V. A. Dudnikov
Dahl Lugansk
National University
Key words: nondeterministic finite automata, graphs,
adjacency table, isomorphism, heuristic algorithms, stochastic discrete
optimization.
This article is devoted to the invariance
binary relation matrices defined on the states of canonical automata which
characterize a given regular language. We give a special definition of
equivalence of such matrices and present algorithms, including heuristic ones,
for computing the amount of non-equivalent matrices. We present the results of
computational experiments on finding the number of possible matrices of binary
relations on the states of canonical automata for dimensions up to 4×5, which show the ways
of getting similar results for higher dimensions when using better computing
devices.
Bibliogr.: 14 refs.
Evaluation of Possible Ranges for the Output
of Linear Systems Subjected to Exogenous Disturbances
P. S. Shcherbakov
Institute of Control Sciences RAS, Moscow
cavour118@mail.ru
Key words: Linear systems, exogenous disturbances, rechability sets.
We consider linear systems with nonzero
initial conditions and subjected to bounded persistent exogenous disturbances.
A transparent and computationally simple method is proposed for the
component-wise evaluation of the output vector.
Bibliogr.: 14 refs.