案例推理在认知引擎中的应用

遗传算法可用于认知引擎中传输参数的优化，但随着认知用户数的增加，遗传算法染色体增长，导致算法收敛时间过长，难以满足认知无线电实时通信的需求．以现有认知引擎为基础提出一种新型的认知引擎架构，并将案例推理融入遗传算法中，利用案例推理寻找匹配案例，为遗传算法提供初始种群，减小遗传算法选择初始种群的盲目性．仿真分析结果表明，与仅采用遗传算法的认知引擎相比，融合案例推理的遗传算法构造的认知引擎收敛速度和处理能力有显著提高，效用函数值也有一定增强．

Application of Case-Based Reasoning in Cognitive Engine

Genetic algorithm can be used to solve the transmission parameters optimization problem in cognitive engine. As the cognitive user increases, the ascending chromosome results in long convergence time of genetic algorithm, which can not satisfy the real time communication application of cognitive radio. According to the characteristic of existing cognitive engine, a cognitive engine architecture is proposed, and case based reasoning algorithm is introduced based on that. Then, case based reasoning is utilized to search the matched cases. and provides genetic algorithm with initial population, which avoids the blindness of initial population searching. Simulation results show that compared with the general genetic algorithm, the cognitive engine with the proposed algorithm increases the convergence speed and utility function values and improves the cognitive engine capability significantly.