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Application of Artificial Neural Networks In Differential Pulse Code Modulation Scheme

机译:人工神经网络在差分脉冲代码调制方案中的应用

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In this work an Artificial Neural Network with Radial Basis Function (RBF) is employed to model a predictor, utilized in differential Pulse Code Modulation (DPCM) scheme. The RBF predictor estimates the magnitude of signal incoming to DPCM, the error between incoming signal and estimated one is applied to quantiser unit. The resultant error contains a few bit word length and is transmitted in data format towards a receiver unit. The output of the RBF predictor is added to error at the receiver part of DPCM to produce actual output. In this study application of RBF predictor in DPCM is explained. The potential offered by DPCM scheme using RBF predictor leads to a considerable amount of reduction in word length of filter coefficients.
机译:在这项工作中,采用具有径向基函数(RBF)的人工神经网络来模拟用于差分脉冲代码调制(DPCM)方案的预测器。 RBF预测器估计输入到DPCM的信号的大小,输入信号与估计的误差被应用于量子单元。得到的误差包含几个字长度,并以数据格式朝向接收器单元发送。 RBF预测器的输出被添加到DPCM的接收器部分以产生实际输出的错误。在该研究中,解释了RBF预测器的应用。使用RBF预测器提供的DPCM方案提供的潜力导致滤波器系数的单词长度相当大的减少。

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