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6 Result(s)

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  1. No Access

    Article

    Extreme learning machine for interval neural networks

    Interval data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy, or variability must be taken into account. Considered in this paper is the learni...

    Dakun Yang, Zhengxue Li, Wei Wu in Neural Computing and Applications (2016)

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    Chapter and Conference Paper

    Solving Path Planning of UAV Based on Modified Multi-Population Differential Evolution Algorithm

    In this paper we solve the path planning of Unmanned Aerial Vehicle (UAV) using differential evolution algorithm (DE). Based on traditional DE, we proposed a modified multi-population differential evolution al...

    Zhengxue Li, Jie Jia, Mingsong Cheng, Zhiwei Cui in Advances in Neural Networks – ISNN 2014 (2014)

  3. No Access

    Chapter and Conference Paper

    The Binary Output Units of Neural Network

    When solving a multi-classification problem with k kinds of samples, if we use a multiple linear perceptron, k output nodes will be widely-used. In this paper, we introduce binary output units of multiple line...

    Qilin Sun, Yan Liu, Zhengxue Li, Sibo Yang… in Advances in Neural Networks – ISNN 2013 (2013)

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    Chapter and Conference Paper

    Convergence Analysis for Feng’s MCA Neural Network Learning Algorithm

    The minor component analysis is widely used in many fields, such as signal processing and data analysis, so it has very important theoretical significance and practical values for the convergence analysis of t...

    Zhengxue Li, Lijia You, Mingsong Cheng in Advances in Neural Networks – ISNN 2013 (2013)

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    Chapter and Conference Paper

    Convergence of Batch BP Algorithm with Penalty for FNN Training

    Penalty methods have been commonly used to improve the generalization performance of feedforward neural networks and to control the magnitude of the network weights. Weight boundedness and convergence results ...

    Wei Wu, Hongmei Shao, Zhengxue Li in Neural Information Processing (2006)

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    Chapter and Conference Paper

    Convergence of an Online Gradient Method for BP Neural Networks with Stochastic Inputs

    An online gradient method for BP neural networks is presented and discussed. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of determi...

    Zhengxue Li, Wei Wu, Guorui Feng, Huifang Lu in Advances in Natural Computation (2005)