type: presentations

label: url

en: Backward Representation of Linear-Time-Varying Stochastic System

en: 63rd Annual Conference of SICE

ja: 63rd Annual Conference of SICE

en: Kochi

ja: Kochi

en: This paper studies a backward representation for a linear-time-varying (LTV) stochastic system. A backward representation for an LTV stochastic system is derived from the computation of covariance matrices by extending stochastic realization theory for linear-time-invariant systems.


en: Numerical Analysis of the Estimate of Noise Covariance by using Best Linear Unbiased Estimate

en: 2022 IEEE Conference on Control Technology and Applications

en: IEEE

ja: IEEE

en: Trieste

ja: Trieste

en: This paper derives a BLUE (best linear unbiased estimate) for the estimation of noise covariance matrix in the framework of subspace identification. An estimation method of the noise covariance with a CCA (canonical correlation analysis) weighting matrix was previously proposed by the authors. The newly derived BLUE is compared with the previously proposed method numerically and it is shown that the previously proposed method gives a good performance.


en: On error analysis of a closed-loop subspace model identification method

en: 24th International Symposium on Mathematical Theory of Networks and Systems

en: Cambridge

ja: Cambridge

en: This paper studies error analysis and asymptotic variance of a closed-loop subspace model identification method for a system described with the output-error state-space representation. For details, since the procedure of the identification method includes the QR factorization of stacked data Hankel matrices, this study investigates asymptotic properties of block elements of the triangular matrix obtained from the QR factorization. The set of the block elements is separated into two components, namely, the signal-based component and the noise-based component. The contributions are to derive asymptotic properties of both components and to obtain the asymptotic covariance matrix of the vectorization of the noise-based component.


en: Consistent Estimate of Kalman Gain in Subspace Identification Method

en: The 2015 IEEE Control Systems Society; Multiconference on Systems and Control

en: IEEE CSS

ja: IEEE CSS

en: Sydney

ja: Sydney

en: This paper proposes a consistent estimates of the Kalman gain and the covariance of the innovations in the framework of subspace identification. The estimates of the system matrices in PO-MOESP method are known to be consistent while the estimates of (A,C) matrices in N4SID method are asymptotically consistent, i.e., each estimate converges in probability to the true value if not only the data length but also the past horizon goes to infinity. The conventional estimates of the Kalman gain and the innovations covariance are asymptotically consistent. To consider the gap relating amount, consistent estimates of the Kalman gain and the innovations covariance are obtained.


en: Error analysis of state approaches in subspace identification methods

en: Proceedings of the SICE Annual Conference


en: Identification of a Hopping Machine Based on a Piecewise Affine Model

en: SICE Annual Conference 2013

ja: SICE Annual Conference 2013

en: The Society of Instrument and Control Engineers

en: Nagoya

ja: Nagoya

en: In this study, a hopping machine (HM) is identified as a piecewise affine (PWA) system via estimating a transition time. The HM can be modeled as the PWA model with two types of discrete transitions: one caused by the change between discrete free-fall and spring-mass states and the other caused by inversion of the sign of a nonlinear drag coefficient term owing to air resistance. First, the equation of motion for the HM is obtained considering the nonlinear term in order to analyze those discrete transitions. In addition, the transition times are estimated using a wavelet-based method proposed in our previous papers. Finally, the HM is identified as a piecewise affine auto regressive exogenous (PWARX) model on the basis of the analyzed discrete states.


en: Asymptotic Properties of MOESP-type Methods

en: Preprints of ALCOSP and PSYCO 2013

ja: Preprints of ALCOSP and PSYCO 2013

en: Caen

ja: Caen

en: Precision of the estimated plant model is often the main interest of system identification. In order to take the predictable part of the noise into account, the noise model is estimated together with the plant model by using ARMAX model. In that case, a model reduction procedure will be required in order to obtain the plant model. On the other hand, the plant model can be estimated directly by using output error (OE) model. In this paper, PI-MOESP method and PO-MOESP method are compared by analysing the signal and noise components of the estimated plant model under the assumption that there are no common poles in the plant and the noise models. The magnitude of the noise component in each method is discussed when the past or future horizon varies and it is shown that there is a possibility that PI-MOESP method gives better performance than PO-MOESP method.


en: Continuous-time model identification by using adaptive observer

en: Preprints of 13th IFAC Symposium on System Identification

en: International Federation of Automatic Control

en: Rotterdam

ja: Rotterdam

en: This paper proposes a continuous-time model identification from sampled I/O data by using an adaptive observer. The boundedness of the parameter and the exponential convergence of the parameter estimate error to 0 under the PE assumption are guaranteed. In order to identify the plant from a finite number of the I/O data, an adaptive observer of a backward system is also proposed.


en: Covariance Analysis of the Estimated System Matrices in a Closed-Loop Subspace Identification Method --- Estimation of the Covariance of (B,D) Matrices

ja: ある閉ループ部分空間同定法における推定されたシステム行列の共分散解析 --- (B,D)行列の共分散の推定

en: 第12回計測自動制御学会制御部門マルチシンポジウム

ja: 第12回計測自動制御学会制御部門マルチシンポジウム

en: Osaka

ja: 大阪


en: Numerical analysis of state-term estimation in closed-loop LPV identification

ja: 閉ループLPV同定における状態項に関する数値的解析

en: 第10回計測自動制御学会制御部門マルチシンポジウム

ja: 第10回計測自動制御学会制御部門マルチシンポジウム

ja: 計測自動制御学会

en: Kusatsu

ja: 草津

en: This paper considers numerical analysis of state-term estimation in a closed-loop LPV (linear Parameter-Varying) identification method presented by the authors. Based on predictor-based system identification (PBSID) for an LTI (Linear-Time-Invariant) system, computation of the intersection between the pas and future data is motivated. The closed-loop LPV identification method presented by the authors is moreover reviewed, and the past and future part of the Gram matrices are derived. A computation method of the intersection between them is presented.


en: Frequency Domain Uncertainty Estimation in a Closed Loop Subspace Identification Method

ja: ある閉ループ部分空間同定法における周波数領域での不確かさの推定

en: 第10回計測自動制御学会制御部門マルチシンポジウム

ja: 第10回計測自動制御学会制御部門マルチシンポジウム

en: The Society of Instrument and Control Engineers

en: This paper proposes an estimate of the covariance matrix of the estimated system matrices in a closed-loop identification method previously proposed by the authors. The estimate of the covariance matrix can be derived from a set of input/output data used for the identification. It is important to provide a covariance matrix of the estimate in order to guarantee the quality of the estimate. Based on the estimated covariance matrix, the uncertainty of the estimated transfer function is derived. Numerical simulations illustrate the validity of the derived covariance estimate.


en: Identification of Nonlinear ARX model using kernel method with random Fourier features

ja: 乱択化フーリエ特徴関数によるカーネル法を用いた非線形ARXモデルの同定

en: 第65回自動制御連合講演会

ja: 第65回自動制御連合講演会

en: Utsunomiya

ja: 宇都宮

en: This paper studies identification of nonlinear-ARX (NARX) model using a kernel method. Random Fourier features are applied to identification of a NARX model, since the size of the matrix in computing regression becomes large in kernel methods, and the numerical computation time is long. Numerical simulation is given to evaluate effectiveness of the present method.


en: Covariance estimation of the estimated extended observability matrix in Closed-Loop MOESP

ja: Closed-Loop MOESP法における拡大可観測性行列推定値の共分散行列の推定

en: 第65回自動制御連合講演会

ja: 第65回自動制御連合講演会

en: It is important to provide a covariance of the estimates to ensure the quality of the identification results. This paper proposes a formula of the covariance of the estimate of the extended observability matrix in Closed-Loop MOESP. The derived formula uses the gap between singular subspaces to estimate the perturbation of the extended observability matrix. The derived formula can be used to estimate its covariance matrix from a single identification result. Numerical simulation compares the sample covariance with the derived formula


en: Consistent Estimate of Covariance Matrix of Innovations Process

ja: イノベーション過程の共分散行列の一致推定値

en: 第58回自動制御連合講演会

ja: 第58回自動制御連合講演会

en: The Institute of Systems, Control and Information Engineers

ja: システム制御情報学会

en: Kobe

ja: 神戸


en: Error Analysis of PO-MOESP method in Closed Loop Environment --- Analysis on the estimates of A and C matrices

ja: 閉ループ環境におけるPO-MOESP法の誤差解析 --- A, C行列の推定値の解析

en: The 2nd Multi-symposium on Control Systems

ja: 第2回制御部門マルチシンポジウム

en: Tokyo

ja: 東京


en: OE/ARMAX

ja: On the precision of the plant estimates based on OE/ARMAX models

en: 第56回自動制御連合講演会

ja: 第56回自動制御連合講演会

en: 新潟

ja: 新潟

en: Identification method based on ARMAX model gives better performance when the system is contaminated by a colored noise because noise model as well as plant model is estimated. However, in order to obtain an plant model, some process such as model reduction or decoupling will be required. Thus, the influence of such process to the precision of the plant model must be analyzed. This paper analyzes the estimation error in $A$ matrix of the plant model when OE model and ARMAX are adopted.


en: Web アプリケーションのデバッグ・保守支援

ja: Web アプリケーションのデバッグ・保守支援

en: 電気関係学会四国支部連合大会

ja: 電気関係学会四国支部連合大会

en: 徳島

ja: 徳島


en: Asymptotic Variance Analysis of Bias Compensated State Space Model Identification Method

ja: バイアス保証型部分空間同定法の漸近的分散解析

en: 第55回自動制御連合講演会

ja: 第55回自動制御連合講演会

en: Kyoto

ja: 京都

en: Based on the analysis of asymptotic variance in bias compensated state space model identification(BCSS) method, PI-MOESP method and PO-MOESP method are compared under the condition that there are no common poles in plant model and noise model. It is shown that there is a possibility that PI-MOESP method gives better performance than PO-MOESP and a numerical example is given to show such case.


en: Variance Analysis of Bias Compensated State Space Model Identification Method --- Case of 1st order system

ja: バイアス補償型部分空間同定法の分散解析 --- 1次系の場合

en: 40th SICE Symposium on Control Theory

ja: 第40回制御理論シンポジウム資料

en: Osaka

en: A method of bias compensation in subspace identification method is introduced and an analysis on its variance is presented. The noise is assumed to be a 0 mean colored noise and is assumed to be uncorrelated with the input. The variance of the extended observability matrix is analyzed and a closed form of the variance is derived for the case when the plant is a 1st order system and the noise is white.


en: An Approach to Design Pattern-Based Refactoring

ja: デザインパターンに基づくソフトウェア・リファクタリング方式の提案

en: 電気関係学会四国支部連合大会

ja: 電気関係学会四国支部連合大会


en: Prediction of Stock Prices Change by Neural Network

ja: ニューラルネットワークによる株価変動の予測

en: 第52回自動制御連合講演会講演論文集

ja: 第52回自動制御連合講演会講演論文集


en: Consturuction and verification of stock prices forecast system using neural networks

ja: ニューラルネットワークを用いた株売買支援システムの構築と検証

en: 第52回自動制御連合講演会講演論文集

ja: 第52回自動制御連合講演会講演論文集


en: Application of Continuous Action Learning Automata in Global Optimal Solution Search of Unknown Function

ja: 未知関数の大域的最適解探索への連続動作学習オートマトンの適用

en: 第52回自動制御連合講演会講演論文集

ja: 第52回自動制御連合講演会講演論文集


en: Construction of Autonomous Transfer System by Plural Movement Bodies Using Stochastic Real-Valued Unit

ja: Stochastic Real-Valued ユニットを用いた複数移動体の自律移動システムの構築

en: 第52回自動制御連合講演会講演論文集

ja: 第52回自動制御連合講演会講演論文集

en: The Institute of Systems, Control and Information Engineers


en: The area weather forecast system based on improvement-type reinforcement learning unit

ja: 改良型強化学習ユニットを用いた地域気象予測システム

en: 第49回自動制御連合講演会

ja: 第49回自動制御連合講演会

en: Kobe