Data-protection ordering/disordering of a fuzzy logic model in a robotic agent via the optical-data-transfer line

Antipov, A. L.; Bykovsky, Alexey Yu.; Egorov, Anton

doi:10.1007/s10946-008-9022-5

Cited by 1 publication

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“…The secured transmission of fuzzy rule, linguistic variables, and membership functions was also adapted for MVL secret coding [79]. This algorithm was proposed for controlled ordering/disordering of the structure of fuzzy logic knowledge base, providing remote switching off/switching on of robotic agents.…”

Section: Mvl-based Schemes For Secured Coding Of Datamentioning

confidence: 99%

“…Moreover, fuzzy logic has made impressive advances mainly as a method to process uncertain and approximate data. As fuzzy logic rules 'If... Then...' and membership functions [40][41][42] can be easily described by MVL expressions for control procedures and secret coding schemes [79,106], the relevant solution seems to use an MVL switching scheme for the activation of specialized fuzzy logic modules. For global networks and pattern recognition tasks, it is also convenient to exploit large capacity discrete models for the switching of fuzzy rules and membership functions.…”

Section: Types Of Logic Models and The Specifity Of Multiple-valued Amentioning

confidence: 99%

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Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Bykovsky

2020

Quantum Reports

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Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.

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Section: Mvl-based Schemes For Secured Coding Of Datamentioning

confidence: 99%

Section: Types Of Logic Models and The Specifity Of Multiple-valued Amentioning

confidence: 99%

Data-protection ordering/disordering of a fuzzy logic model in a robotic agent via the optical-data-transfer line

Cited by 1 publication

References 4 publications

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

Heterogeneous Network Architecture for Integration of AI and Quantum Optics by Means of Multiple-Valued Logic

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