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Stochastic Configuration Networks: Fundamentals and Algorithms
IEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2017
10 February 2017
Dianhui Wang
Ming Li
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Papers citing
"Stochastic Configuration Networks: Fundamentals and Algorithms"
50 / 60 papers shown
Title
Broad stochastic configuration residual learning system for norm-convergent universal approximation
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Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems
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Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling
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Recurrent Stochastic Configuration Networks for Temporal Data Analytics
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Stochastic Configuration Machines: FPGA Implementation
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Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
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Stochastic Configuration Machines for Industrial Artificial Intelligence
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Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
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Interpretable Neural Networks with Random Constructive Algorithm
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Label-Efficient Learning in Agriculture: A Comprehensive Review
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Randomly Initialized Subnetworks with Iterative Weight Recycling
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Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning
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Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint
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Wenzhao Zheng
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Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics
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Muhammad H. Alkhudaydi
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A Modified Batch Intrinsic Plasticity Method for Pre-training the Random Coefficients of Extreme Learning Machines
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Cluster-based Input Weight Initialization for Echo State Networks
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Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis
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Q. M. J. Wu
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General stochastic separation theorems with optimal bounds
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How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
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Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
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A Constructive Approach for Data-Driven Randomized Learning of Feedforward Neural Networks
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Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It
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