动态修正下的神经网络盲均衡算法

针对现有的神经网络算法收敛速度慢以及精确度低的问题，通过对传统的神经网络盲均衡算法以及前馈神经网络进行研究，提出一种具有自动修正效果的前馈神经网络盲均衡算法。该算法通过对算法中的代价函数以及迭代步长因子进行改进，来提高算法的收敛速度；通过对所获得的目标信号进行修正处理，来对所获取的信息进行修正。实验结果表明，该算法的实验结果与预期效果基本相符，具有可靠性强、收敛速度快的优势。

Neural Network Blind Equalization Algorithm Under Dynamic Correction

Existing neural network algorithms have the problems of slow convergence speed and low accuracy. In response to this phenomenon, we study traditional neural network blind equalization algorithms and feed forward neural networks, and present a feed forward neural network blind equalization algorithm with the effect of automatic correction. The convergence speed of the algorithm is improved by improving the cost function and iterative step factor. The acquired information is corrected by correcting the acquired target signals. Experimental results show that they are basically consistent with the expected results, and it is of the advantages of high reliability and fast convergence speed.