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      Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

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      Transactions of Nonferrous Metals Society of China
      Elsevier BV

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          A general regression neural network.

          A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. It is shown that, even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified.
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            Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

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              Experimental study on acoustic emission (AE) characteristics and crack classification during rock fracture in several basic lab tests

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                Author and article information

                Journal
                Transactions of Nonferrous Metals Society of China
                Transactions of Nonferrous Metals Society of China
                Elsevier BV
                10036326
                June 2021
                June 2021
                : 31
                : 6
                : 1766-1783
                Article
                10.1016/S1003-6326(21)65615-7
                67cd1922-5aff-4d5a-8b11-8d040d8b9253
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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