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Modeling microphone in PSpice based on Neural Network

机译:基于神经网络的PSPICE中建模麦克风

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Presently, the electret condenser microphone (ECM) is used in almost every consumer and communication audio application. In order to improve the efficiency of the circuit design, circuit simulation is necessary. In this article, we present a method based on Neural Network for modeling ECM, by which the fundamental characteristics of an ECM, including sensitivity, directivity, output impedance and frequency response, are modeled in PSpice. Firstly, each inputoutput characteristic is approximated with different Neural Network, after which the structures, weights and biases of the Neural Network depicting different characteristic of the ECM are acquired. Secondly, the structures are described in PSpice language to form sub-circuits respectively. Finally, these subcircuits are integrated into a unitary sub-circuit based on the relationship between the inputs and output of the ECM to form the final model.
机译:目前,在几乎每个消费者和通信音频应用中使用驻极体冷凝器麦克风(ECM)。为了提高电路设计的效率,需要电路模拟。在本文中,我们介绍了一种基于神经网络的用于建模ECM的方法,通过它在PSPice中建模了ECM的基本特征,包括灵敏度,方向性,输出阻抗和频率响应。首先,通过不同的神经网络近似每个输入输出特性,之后获取描绘ECM的不同特征的神经网络的结构,权重和偏差。其次,在PSPice语言中描述了结构以分别形成子电路。最后,基于ECM的输入和输出之间的关系集成到整体子电路中以形成最终模型。

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