基于粗神经网络的仿人智能机器人的语音融合算法研究

Speech fusion based on rough neural network for humanoid intelligent robots

将粗集合理论与神经网络相结合 ,提出一种基于粗神经网络的新的信息融合方法 ,用于仿人智能机器人的语音融合。该方法不仅可以接受定量输入 ,而且可以接受定性输入 ,即输入是一个范围 ,或在观测时间内输入是变化的。由于粗神经网络的误差传递函数不可微 ,所以采用遗传算法来训练粗神经网络。仿真实验结果表明 。

Integrating rough set theory with neural network theory, a novel information fusion method based on rough neural network is proposed. It is used in speech fusion of humanoid intelligent robots. The input could be qualitative, i.e. a range or variational during the observation, as well as quantitative. Because the error transfer function of rough neural network is not differentiable, genetic algorithms are applied for training the network. The simulation results indicated that the novel information fusion method based on rough neural network does improve the speech recognition probability.