Opportunistic Knowledge Adaption in Self-Learning Systems

THE CONCEPT OF MANAGING THE DATA NETWORK BASED ON SELF-LEARNING SYSTEMS

Труды НИИР ◽

10.34832/niir.2020.2.3.003 ◽

2020 ◽

Author(s):

С.Р. РОМАНОВ

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Transmission ◽

Network Control ◽

Learning Systems ◽

Optimal Routing ◽

Transmission Network ◽

Data Traffic ◽

Data Network ◽

Self Learning

Рассмотрен принцип управления сетью передачи данных (СПД)с помощью искусственной нейронной сети. Предложена концепция проведения вычислений при решении задачи оптимальной маршрутизации трафика данных. Приведен алгоритм управления сетью СПД на базе нейронной сети Хэмминга. The principle of data transmission network control using an artificial neural network is considered. The concept of carrying out calculations when solving the problem of optimal routing of data traffic is proposed. The algorithm for controlling the data transmission network based on the Hamming neural network is presented.

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Conceptual Model for Specialized Learning Systems within Organizations

International Journal of Systems and Service-Oriented Engineering ◽

10.4018/ijssoe.2014100102 ◽

2014 ◽

Vol 4 (4) ◽

pp. 19-34

Author(s):

Isabel Mendes ◽

Henrique Santos ◽

Celina Pinto Leão

Keyword(s):

Conceptual Model ◽

Learning Systems ◽

Semantic Technologies ◽

Specialized Knowledge ◽

Know How ◽

Digital Era ◽

Basic Reference ◽

Knowledge Intensive ◽

Self Learning ◽

Content Regulation

This study is focused on the potentiality and benefits that intelligent learning systems can bring to knowledge intensive organizations, in particularly software houses. Therefore the authors will present a conceptual model for the foundation of specialized knowledge systems with dynamic content regulation, oriented to self-learning. Its structure is based on the combination of semantic technologies (e.g. ontologies) and social networks, from where interaction lasts. To achieve that goal, it is important to know how to explore the information in this type of environments, as well as understand which behaviors and trends influence the individuals learning in the digital era. The connectivist theory offers an important contribution to the understanding of this phenomenon, being therefore one of the basic reference in the development of this work.

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Reducing Negative Complexity by a Computational Semiotic System

Semiotics and Intelligent Systems Development ◽

10.4018/978-1-59904-063-9.ch012 ◽

2007 ◽

pp. 330-342 ◽

Cited By ~ 1

Author(s):

Gerd Doben-Henisch

Keyword(s):

Virtual Machines ◽

Fault Tolerant ◽

Visual Programming ◽

State Machine ◽

Learning Systems ◽

Semiotic System ◽

Abstract State Machine ◽

Set Up ◽

Self Learning ◽

Programming Interface

The chapter describes the set-up for an experiment in computational semiotics. Starting with a hypothesis about negative complexity in the environment of human persons today it describes a strategy, how to assist human persons to reduce this complexity by using a semiotic system. The basic ingredients of this strategy are a visual programming interface with an appropriate abstract state machine, which has to be realized by distributed virtual machines. The distributed virtual machines must be scalable, have to allow parallel processing, have to be fault tolerant, and should have the potential to work in real time. The objects, which have to be processed by these virtual machines, are logical models (LModels), which represent dynamic knowledge, including self learning systems. The descriptions are based on a concrete open source project called Planet Earth Simulator.

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