共被期刊论文引用了4次
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| 1 | A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain显示文摘 | Deyi Li Hongbo Gao | 2018 | Engineering2018，4，4： | 13 |
| 2 | Speech recognition algorithm based on neural network and hidden Markov model显示文摘This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model( HMM) and artificial neural network( ANN). First,the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second,with the probability score as input to the neural network,the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally,Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of the neural network,the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover,the hybrid model corrects the individual flaws of the HMM and the neural network,and greatly improves the speed and performance of speech recognition. | Zhao Jianhui Gao Hongbo Liu Yuchao Cheng Bo | 2018 | The Journal of China Universities of Posts and Telecommunications2018，25，4： | 4 |
| 3 | Hadoop平台下加权马氏距离的Web大数据分析研究显示文摘Web大数据具有数据量大、数据异构性强、挖掘难度大等问题,针对如何提高Web大数据聚类分析准确性并保证数据分析的时效性,本文提出一种基于Hadoop平台和加权马氏距离的Web大数据分析方法。该方法在基于Hadoop平台架构上首先对Web大数据进行预处理和数据切片,然后通过计算得到方差贡献率并作为马氏距离计算的权重,最后通过加权马氏距离计算对数据判定聚类。仿真实验表明,所提方法可以有效地保证Web大数据聚类分析的准确性并具有较高的时效性。 | 王艳洁 范存群 | 2018 | 电视技术2018，42，11： | 2 |
| 4 | A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation显示文摘The Extreme Learning Machine(ELM) is an effective learning algorithm for a Single-Layer Feedforward Network(SLFN). It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning(NCL) to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs(HE2 LM) for classification has different ELM algorithms including the Regularized ELM(RELM), the Kernel ELM(KELM), and the L2-norm-optimized ELM(ELML2). The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers. | Adnan O.M.Abuassba Yao Zhang Xiong Luo Dezheng Zhang Wulamu Aziguli | 2017 | Tsinghua Science and Technology2017，22，6： | 1 |
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