期刊名称：Brain. Broad Research in Artificial Intelligence and Neuroscience
印刷版ISSN：2067-3957
出版年度：2011
卷号：2
期号：4
页码：41-47
语种：English
出版社：EduSoft publishing
摘要：Hidden Markov Model (HMM) is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
关键词：Neural Networks, Genetic Algorithms, Neuroevolution, Hidden Markov Model
