【作者】 章文彬；

【导师】 方路平；

【作者基本信息】 浙江工业大学 ， 计算机应用技术， 2007， 硕士

【摘要】 近年来，基于spike神经元模型的人工神经网络(Spiking NeuralNetworks，简称SNNs，我们称之为脉冲神经网络)受到了人们的很大关注，被誉为下一代神经网络。spike神经元模型是利用神经元触发的脉冲时序来进行信息的编码和处理，而不是用触发脉冲的平均速率来进行编码。因此SNNs编码方式具有很好的时态性，非常适合分析处理基于时间结构的数据。有研究表明SNNs比一般的神经网络具有更强的计算能力，而且前者所需神经元数目或层次也比后者要少。不过目前对SNNs的研究主要是理论、算法等方面，在实际应用方面的研究比较少。本文结合了脉冲神经网络处理时态问题上优势，以及最近学术界的研究的热点，就基于脉冲神经网络这一理论来解决语音识别问题进行探讨和研究。本文系统地介绍了脉冲神经网络的相关理论，包括Spike神经元的模型，脉冲编码方式，动力学原理及表示，著名的H-H方程，以及网络结构和相关应用。较全面地介绍了语音识别技术，并分析了语音识别所面临的问题及前景和应用。清晰地给出了用脉冲神经网络来进行语音识别的方法和步骤，在脉冲神经元的仿真上使用比较经典的H-H方程，同时利用圆映射的方法将神经脉冲序列转变为符号序列，最终由符号空间转变到距离空间来进行计算和匹配。最后用软件完成了基于脉冲神经网络理论的孤立词语音识别的实验，通过调节H-H方程的部分参数提高了系统的识别率。更多还原

【Abstract】 Recently, Spiking Neural Networks are considered as a new computation paradigm, representing the next generation of Artificial Neural Networks by offering more flexibility and degrees of freedom for modeling computational elements. Neurological research has shown that spike neurons encode information in the timing of single spikes, and not in their average firing frequency. So the encoding type of SNNs is temporal, and SNN models fits to the analysis of time-structured data very much. Spiking neural networks have been shown to have more powerful computation capability than their non-spiking predecessor as their can use less neurons to solve the same problem. But as far as we know, most researches have been restricted to theoretical work, and there are few applications of SNNs to real-life data. Therefore, we combine the advantage of SNNs to solve temporal problem with the research hotspot of the academia to do some research on Speech Recognition based on spiking neural networks.First, this article gives an introduction about the SNNs, including the model of the spike neuron, spikes coding, neuronal dynamics, Hodgkin-Huxley Model, Network architecture and Applications using SNNs. Also, it gives a comprehensive introduction about speech recognition technology, and has analyzed the problem which the speech recognition is faced with. Clearly, it proposes the approach and the algorithm to do speech recognition based on SNNs, using the H-H equations to simulate the spike neuron and using the circle-map to transform the pulses list to symbol list and computing. At last, the recognition system has been implemented in Matlab, and parameters of H-H equation have been adjusted to improve the results of therecognition.更多还原