Researcher Database | Faculty of Engineering Department of Electrical and Electronic Engineering | Yoshitaka Ito |

（Last updated : 2026-05-16 12:55:54）

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Yoshitaka Ito

Department / Course Hokkaido University of Science Faculty of Engineering Department of Electrical and Electronic Engineering

Job Associate Professor

Academic background
2019/03/18 Degree Acquisition
2011/03 Degree Acquisition

Present specialized field
Others, Others

Belonging society
2022/08 ～	The Institute of Electrical Engineers of Japan

Book and thesis
Papers	A performance evaluation method for one-parameter bifurcation diagrams reconstructed by Extreme Learning Machine with Input Parameter Channels (Co-authored) 2026/02/27
Papers	Performance evaluation of a classifier using LSTM autoencoder for ECG waveforms measured through cloth (Co-authored) 2026/02/27
Papers	Data-Driven Guidelines for Buffer Sizing: A Dynamical System Approach for Throughput Time-Series (Co-authored) 2026/01/12
Papers	Investigating the Correspondence between Original Parameter Space and Synaptic Weight Space for Parameter Space Estimation in Predicting Critical Transition (Sole-authored) 2026/01/01
Papers	Normal-Abnormal Classification of ECG5000 by Estimating Parameters Based on the Bifurcation Dia- gram Reconstruction (Sole-authored) 2025/10/27
Display All（27）

Academic conference presentation
2021/10/26	Modeling of Quantum Electron Transmission Process in Two-dimensional Nanowire System using Recurrent Neural Network, MNC2021, remote, 2021. (34th International Microprocesses and Nanotechnology Conference)
2021/03	Relationship between Success Rate of Bifurcation Diagrams Reconstruction and Synaptic Weight Vectors of Predictor (RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing 2021)
2020/11	Reconstructing bifurcation diagrams of all components in Rossler equations only from time-series data sets (2020 International Symposium on Nonlinear Theory and its Applications)
2020/03	Effects of Omitting Pruning Procedure on Extreme Learning Machine for Bifurcation Diagrams (RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing 2020)
2018/11	Reconstructing Bifurcation Diagrams of a Chaotic Neuron Model Using an Extremely Learning Machine (The 9th International Conference on Extreme Learning Machines)
Display All（12）