一种改进神经网络算法在通信噪声信号分类中的应用

Changren Yu, Lianxing Jia, Yintao Hou
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引用次数: 0

摘要

利用遗传算法对神经网络进行改进,得到最优权值和阈值。使用生成的最优权值和阈值来运行神经网络,可以减少使用随机权值和阈值的神经网络的误差范数。利用该算法对三种通信噪声信号进行了分类仿真,验证了该算法的准确性。实验表明,该算法在通信领域的噪声信号分类中具有良好的应用前景。
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Application of an Improved Neural Network Algorithm in Classification of Communication Noise Signals
The genetic algorithm is used to improve the neural network and obtain the optimal weights and thresholds. Using the generated optimal weights and thresholds to run the neural network reduces the error norm over neural network which use random weights and thresholds. The classification of three kinds of communications noise signals was simulated by this algorithm, and the accuracy of this algorithm was proved. Experiments show that the algorithm will have an excellent application prospect in the noise signals classification in the communications field.
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