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在无线通信网络中,由于通信网络信道具有异构性和自组织特征,造成通信信道的脉冲响应数据产生数据特征随机。传统基于神经网络控制算法的通信网络信道脉冲响应数据挖掘时,脉冲响应参数受到这种随机特征的干扰,导致数据挖掘性能不好。提出基于遗传适应度概率正则迁徙控制的通信网络信道脉冲响应数据挖掘算法。构建无线通信网络信道模型,提取通信信道的脉冲响应数据特征,设计遗传算法进行通信信道的脉冲响应适应度正则迁徙控制,实现数据挖掘算法改进。仿真实验表明,采用该算法进行无线通信网络的信道响应数据挖掘,数据挖掘精度较高,降低通信误码率,提高了通信网络的信道均衡控制能力。
In the wireless communication network, the impulse response data of the communication channel generates data features randomly due to the heterogeneous and self-organizing features of the communication network channels. When the conventional neural network based control algorithm for channel impulse response data mining of communication networks, the impulse response parameters are disturbed by such random features, resulting in poor data mining performance. A data mining algorithm for impulse response of communication network channel based on genetic algorithm is presented. The wireless communication network channel model is constructed, the impulse response data characteristic of the communication channel is extracted, and the genetic algorithm is designed to control the impulse response fitness of the communication channel and to improve the data mining algorithm. Simulation results show that the proposed algorithm performs channel response data mining in wireless communication networks with high accuracy of data mining, lowers the bit error rate of communication and improves the channel balance control of communication networks.