A Self-Learning Method for Cognitive Engine Based on CBR and Simulated Annealing

Yi Jing Liu; Li Ya Chai; Jing Min Liu; Bo Wen Li

doi:10.4028/www.scientific.net/AMR.457-458.1586

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 457-458A Self-Learning Method for Cognitive Engine Based...

A Self-Learning Method for Cognitive Engine Based on CBR and Simulated Annealing

Abstract:

The essential difference of cognitive radio from traditional radio lies in its ability to sense, learn and adapt to the environment. Recently, the research for cognitive radio has focused on the configuration problems of multi-objective optimization. However, in actual communication systems, the observable environment parameters are limited. Besides, the relationship between the system’s inputs and outputs is often complicated. Thus, Cognitive radio (CR) needs to understand and adapt to the environment through learning. To solve the problem mentioned above, a self-learning method for Cognitive radio decision engine based on CBR and Simulated Annealing is proposed. The simulation results show that the proposed method has the advantages of self-learning, multi-objective adaptation and rapid convergence.

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Periodical:

Advanced Materials Research (Volumes 457-458)

Pages:

1586-1594

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.457-458.1586

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Online since:

January 2012

Authors:

Yi Jing Liu, Li Ya Chai, Jing Min Liu, Bo Wen Li

Keywords:

Artificial Intelligence (AI), Case-Based Reasoning (CBR), Cognitive Engine, Cognitive Radio (CR), Simulated Annealing (SA)

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