基于SFLA优化BP神经网络的语音信号分类

BP神经网络是一种多层前馈网络,数据经过网络的输入层、隐含层逐层处理后,由输出层进行输出,通过和期望输出的对比进行反向传播,调整网络参数使输出不断逼近期望输出；在使用BP神经网络对语音特征信号进行分类的过程中,会出现BP神经网络易陷入局部最优解、学习收敛速度慢的问题；针对此问题提出一种基于SFLA优化BP神经网络权值和阀值的方法,引入SFLA算法优化网络权值和阀值,利用SFLA优化后的BP网络模型进行语音特征信号分类；仿真结果表明,经SFLA优化后的BP神经网络与未优化的神经网络相比,不仅训练速度快, 而且误差小,语音特征信号分类的正确率平均提高1.31%。

Classification of Speech Based on BP Neural Network Optimized by SFLA

A back-propagation (BP) neural network consists of an input layer,one or more hidden layers and an output layer.An input vector is presented to the network, it is propagated forward through the network, layer by layer, until it reaches the output layer. The output of the network is then compared to the desired output, using a loss function, The error values are then propagated backwards, starting from the output, until each neuron has an associated error value which roughly represents its contribution to the original output.The BP neural network easily falls into a local extreme values and the slow convergence,during the Classification of Speech using it.A new method is put forward to optimize weights and threshold of BP neural network using SFLA. The new model was used in the classification of four typical speech, results of which were analysed and compared with that BP neural network. BP neural network based on SFLA has both fast training speed and small number of errors, produced average increase of 1.31 % in the accuracy.