CAJ | 학술논문

认知引擎是认知无线电关键技术之一，其核心是利用人工智能算法完成认知学习、推理与决策功能。自适应满足环境变化和用户需求。提出一种基于ARTl和FAM神经网络的认知引擎。该引擎基于MATLAB802．1la仿真平台模拟无线通信环境，通过对环境信息的学习训练，结合信道特征和用户需求推理决策出系统最优工作参数，实现认知无线电自适应配置。仿真结果表明，该认知引擎能有效实现认知无线电学习推理功能，且算法精度和稳定性均优于SVM及BP网络模型。
인지인경시인지무선전관건기술지일，기핵심시이용인공지능산법완성인지학습、추리여결책공능。자괄응만족배경변화화용호수구。제출일충기우ARTl화FAM신경망락적인지인경。해인경기우MATLAB802．1la방진평태모의무선통신배경，통과대배경신식적학습훈련，결합신도특정화용호수구추리결책출계통최우공작삼수，실현인지무선전자괄응배치。방진결과표명，해인지인경능유효실현인지무선전학습추리공능，차산법정도화은정성균우우SVM급BP망락모형。
Cognitive engine (CR) is one of the key technologies of cognitive radio, and its core is utilizing ar- tificial intelligence to achieve the ability of learning, reasoning and decision. This paper presents a cognitive engine based on ART1 and FAM neural network to adapt varied environment and user's needs. In the wireless environment based on 802. 11 a simulation platform, the engine can learn the environment informant, reason the best working parameters and realize the CR configurations on the conditions of channel characters and user' s needs. The simulation results improve that the proposed cognitive engine can effectively realize the learning nnrl r~n~nnin~r fimctinn nnrt it.~ nr~r~urAr~v nnrt ~tnhilitv nre, both ha, Her thnn thn~ nf SVM nnd RP netwnrk.