Evolving Intelligent Systems: Methods, Learning, &amp;amp; Applications

Kasabov, Nikola; Filev, Dimitar

doi:10.1109/isefs.2006.251185

Cited by 46 publications

(26 citation statements)

References 52 publications

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“…There are recognised techniques, such as fuzzy logic, artificial neural networks, machine learning and evolutionary algorithms that contribute to increase a system's 'machine intelligence quotient' [25]. The rationale behind the intelligent label of those techniques is their ability to represent and deal with knowledge [26]. Consequently, in this paper, Artificial Neural Networks (ANN), Fuzzy Logic (FL) are addressed.…”

Section: Design Experimentsmentioning

confidence: 99%

Semantic Adaptation of Knowledge Representation Systems

Lucena

Sarraipa

Jardim‐Gonçalves

2013

IFIP Advances in Information and Communication Technology

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Abstract. Due to the worldwide diversity of enterprises, a high number of ontologies representing the same segment of reality which are not semantically coincident have appeared. To solve this problem, a possible solution is to use a reference ontology to be the intermediary in the communications between the community enterprises and to outside. Since semantic mappings between enterprise's ontologies are established, this solution allows each of the enterprises to keep internally its own ontology and semantics unchanged. However information systems are not static, thus established mappings become obsolete with time. This paper's presents a PhD research with the objective to identify a suitable approach that combines semantic mappings with user's feedback, providing an automatic learning to ontologies & enabling autoadaptability and, consequently, dynamism to the information systems.

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Section: Design Experimentsmentioning

confidence: 99%

Semantic Adaptation of Knowledge Representation Systems

Lucena

Sarraipa

Jardim‐Gonçalves

2013

IFIP Advances in Information and Communication Technology

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“…These adaptive systems keep integrating new data by updating existing rules or by modifying their structure to memorize new rules. As in [18], rules are interpreted as clusters of input/output associations that map portions of the input space to appropriate model functions. Learning is based on a local element tuning.…”

Section: Cognitive Radio and Artificial Intelligencementioning

confidence: 99%

A generic cognitive framework for supervising the radio dynamic reconfiguration

et al. 2009

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Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation that goes beyond traditional system adaptation. So far, a rather limited amount of work has been published on the cognitive mechanisms that should be embedded into communicating equipments to achieve such an intelligent behavior. This paper presents a generic cognitive framework for autonomous decision making with regard to multiple, possibly conflicting, operational objectives in a time-varying environment. The framework is based on the definition of two scales introducing order relationships between the configurations that help the reasoning and learning processes. The resulting cognitive engine learns to progressively identify the optimal configurations for the design objectives imposed given the current radio environment. The proposed approach is illustrated for a case of cognitive waveform design and extensive simulation results validate the cognitive engine behavior.

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“…It should be noted that in the area of decision and control for economic systems and business processes as well as modelling of the respective dynamics, a deep shift towards computational intelligence methods (e. g., see Kasabov and Filev, 2006;Pete and coauthors, 1998;Special Issue on Neural Networks, 2001;Wang and Archer, 1998;Yager, 2004) is under way, along with the newest theoretical advances in methodologies for dealing with perceptions and "computing with words" (Zadeh, 2006) in computing machines. Moreover, these developments are likely to lead to new uses of qualitative information in decision and control techniques for systems with a distributed information base and partially decentralised control system architectures.…”

Section: An Overview Of the Key Problemsmentioning

confidence: 99%

Applied System and Control Sciences to Social Systems: Globalization Age Paradigms

Dimirovski

2008

IFAC Proceedings Volumes

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Modern advanced information technologies resulting from automation of control and decision processes along with ubiquitous communications have a multitude of impacts on development of national economies within the global economy. Thus all kinds of social systems, being essentially human centred systems, is a cross-, inter-and multi-disciplinary challenge to systems and control researchers. Social systems in modern civilization are reviewed from the systems science viewpoint and on the grounds of recent developments in control science and technology and with regard to globalization paradigm. The innovative systems approaches employing results from hybrid systems theory and dynamical networks are needed to address the now-old challenges of combined knowledge and technology transfer world-wide for sustainable development that may remedy climate change and some of the negative socio-economic aspects of globalization.

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Evolving Intelligent Systems: Methods, Learning, & Applications

Cited by 46 publications

References 52 publications

Semantic Adaptation of Knowledge Representation Systems

Semantic Adaptation of Knowledge Representation Systems

A generic cognitive framework for supervising the radio dynamic reconfiguration

Applied System and Control Sciences to Social Systems: Globalization Age Paradigms

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