Training method of tunneling parameter prediction model based on incremental learning and related equipment
The invention provides a training method of a tunneling parameter prediction model based on incremental learning and related equipment. The tunneling parameter prediction precision of a tunneling machine of the model can be improved. The method comprises the steps that an initial tunneling parameter...
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creator | LIU BIN SHENG GUANGZU YU HONGGAN HUANG XING SANG HAOMIN DENG PENGHAI WEN TIAN LIU QUANSHENG YANG TAIHUA |
description | The invention provides a training method of a tunneling parameter prediction model based on incremental learning and related equipment. The tunneling parameter prediction precision of a tunneling machine of the model can be improved. The method comprises the steps that an initial tunneling parameter prediction model is constructed based on a bidirectional long-short time memory network, the tunneling parameter prediction model based on incremental learning comprises an input layer, a first hidden layer, an output layer and N second hidden layers, and activation functions of the N second hidden layers are unsaturated activation functions; target input data is input into the initial tunneling parameter prediction model to obtain predicted tunneling parameters, and the target input data is any data in the standardized model input data set; optimizing a hyper-parameter combination of the initial tunneling parameter prediction model based on a quantum particle swarm algorithm, the predicted tunneling parameters an |
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The tunneling parameter prediction precision of a tunneling machine of the model can be improved. 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The tunneling parameter prediction precision of a tunneling machine of the model can be improved. The method comprises the steps that an initial tunneling parameter prediction model is constructed based on a bidirectional long-short time memory network, the tunneling parameter prediction model based on incremental learning comprises an input layer, a first hidden layer, an output layer and N second hidden layers, and activation functions of the N second hidden layers are unsaturated activation functions; target input data is input into the initial tunneling parameter prediction model to obtain predicted tunneling parameters, and the target input data is any data in the standardized model input data set; optimizing a hyper-parameter combination of the initial tunneling parameter prediction model based on a quantum particle swarm algorithm, the predicted tunneling parameters an</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING EARTH DRILLING ELECTRIC DIGITAL DATA PROCESSING FIXED CONSTRUCTIONS GALLERIES LARGE UNDERGROUND CHAMBERS MINING PHYSICS SHAFTS TUNNELS |
title | Training method of tunneling parameter prediction model based on incremental learning and related equipment |
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