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34th Chinese Control and Decision Conference (CCDC)

作者： Chu, Jie Li, Junhong Zong, Tiancheng Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

ISBN: (纸本)9781665478960

The Volterra model can approximate many nonlinear systems, and it is a typical nonlinear system. This paper studies the parameter estimation problem of the Volterra model. Combining the Levenberg-Marquardt optimization method and the recursive identification method, we propose a Levenberg-Marquardt recursive algorithm and apply it to the identification of the Volterra system. In order to verify the feasibility of the above algorithm, the second-order Volterra system is simulated using the Levenberg-Marquardt recursive algorithm and the forgetting factor stochastic gradient algorithm respectively, and then we compare the simulation results of the Volterra system under the two algorithms. The simulation results show that the above two algorithms can identify the parameters of the Volterra system. Compared with the forgetting factor stochastic gradient algorithm, the Levenberg- Marquardt recursive algorithm has faster convergence speed and higher convergence accuracy. This proves the effectiveness of the Levenberg-Marquardt recursive algorithm.

关键词： parameter estimation nonlinear systems Volterra model Levenberg-Marquardt recursive algorithm stochastic gradient algorithm

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The Cost of Privacy in Asynchronous Differentially-Private Machine Learning

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 2118-2129页

作者： Farokhi, Farhad Wu, Nan Smith, David Kaafar, Mohamed Ali Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Macquarie Univ Dept Comp Sydney NSW 2109 Australia CSIROs Data61 Eveleigh NSW 2015 Australia Australian Natl Univ Coll Engn & Comp Sci CECS Canberra ACT 2600 Australia

We consider training machine learning models using data located on multiple private and geographically-scattered servers with different privacy settings. Due to the distributed nature of the data, communicating with all collaborating private data owners simultaneously may prove challenging or altogether impossible. We consider differentially-private asynchronous algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function. Therefore, the algorithm efficiently scales to many data owners. We define the cost of privacy as the difference between the fitness of a privacy-preserving machine-learning model and the fitness of trained machine-learning model in the absence of privacy concerns. We demonstrate that the cost of privacy has an upper bound that is inversely proportional to the combined size of the training datasets squared and the sum of the privacy budgets squared. We validate the theoretical results with experiments on financial and medical datasets. The experiments illustrate that collaboration among more than 10 data owners with at least 10,000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy. The number of the collaborating datasets can be lowered if the privacy budget is higher.

关键词： Training Data models Distributed databases Biological system modeling Degradation Privacy Machine learning Machine learning differential privacy stochastic gradient algorithm asynchronous

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A Unified Convergence Analysis for Shuffling-Type gradient Methods

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JOURNAL OF MACHINE LEARNING RESEARCH 2021年第1期22卷 1-44页

作者： Nguyen, Lam M. Quoc Tran-Dinh Phan, Dzung T. Phuong Ha Nguyen van Dijk, Marten IBM Res Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27599 USA eBay Inc San Jose CA 95125 USA Ctr Wiskunde & Informat Amsterdam Netherlands

In this paper, we propose a unified convergence analysis for a class of generic shuffling-type gradient methods for solving finite-sum optimization problems. Our analysis works with any sampling without replacement strategy and covers many known variants such as randomized reshuffling, deterministic or randomized single permutation, and cyclic and incremental gradient schemes. We focus on two different settings: strongly convex and nonconvex problems, but also discuss the non-strongly convex case. Our main contribu-tion consists of new non-asymptotic and asymptotic convergence rates for a wide class of shuffling-type gradient methods in both nonconvex and convex settings. We also study uni-formly randomized shuffling variants with different learning rates and model assumptions. While our rate in the nonconvex case is new and significantly improved over existing works under standard assumptions, the rate on the strongly convex one matches the existing best-known rates prior to this paper up to a constant factor without imposing a bounded gradient condition. Finally, we empirically illustrate our theoretical results via two nu-merical examples: nonconvex logistic regression and neural network training examples. As byproducts, our results suggest some appropriate choices for diminishing learning rates in certain shuffling variants.

关键词： stochastic gradient algorithm shuffling-type gradient scheme sampling with-out replacement non-convex finite-sum minimization strongly convex minimization

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Convergence of modified SG algorithm with quantized observations

Convergence of modified SG algorithm with quantized observat...

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第43届中国控制会议

作者： Ren Yan Zhixin Liu Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper considers the identification problem of linear regression model with quantized observations. We propose a modified stochastic gradient algorithm to estimate unknown parameters using the quanized observations. Under the "weakest"excitation condition, we show that the proposed algorithm converges to the true parameters without requiring the commonly used i.i.d. conditions. Based on this, the convergence rate is further considered. Finally, simulation results are given to demonstrate the effectiveness of the proposed algorithm.

关键词： stochastic gradient algorithm quantized observations convergence rate parameter identification stochastic dynamic system

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Fast Distributed stochastic Scheduling for A Multi-Energy Industrial Park 4

Fast Distributed Stochastic Scheduling for A Multi-Energy In...

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4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Zhu, Dafeng Yang, Bo Wang, Zhaojian Ma, Chengbin Ma, Kai Zhu, Shanying Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9781728162072

The multi-energy management framework of industrial parks advocates energy conversion and scheduling, which takes full advantage of the compensation and temporal availability of multiple energy. However, how to exploit elastic loads and compensate inelastic loads to match multiple generators and storage is still a key problem under the uncertainty of demand and supply. To solve the issue, the energy management problem is constructed as a stochastic optimization problem. The optimization aims are to minimize the time-averaged energy cost and improve the energy efficiency while respecting the energy constraints. To achieve the distributed implementation in real time without knowing any priori knowledge of underlying stochastic process, a distributed stochastic gradient algorithm based on dual decomposition and a fast scheme are proposed. The numerical results based on real data show that the industrial park, by adopting the proposed algorithm, can achieve social welfare maximization asymptotically.

关键词： Multi-energy management framework industrial park distributed implementation stochastic gradient algorithm

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Predicting Hepatitis B to be acute or chronic in an infected person using machine learning algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2022年 172卷

作者： Vijayalakshmi, C. Mohideen, S. Pakkir BSA Crescent Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India BSA Crescent Inst Sci & Technol Dept Comp Applicat Chennai Tamil Nadu India BSA Crescent Inst Sci & Technol Chennai Tamil Nadu India

Hepatitis B is a viral infection which causes liver damage. It can lead to death. This hepatitis B along with Hepatitis C can cause hepatocellular carcinoma and liver cirrhosis. In this paper it is discussed about Hepatitis B found positive in a person's blood test is acute or chronic. This research work plans to code an endurance forecast model for the dataset which contains the boundaries or data of Hepatitis-B patients. At first the information will be pre-prepared, to improve fit for additional handling and for being in satisfactory configuration for the calculations. At that point, several calculations to indicate the forecast and draw out the precision of the model. What's more, further contrast those calculations with indicate the calculation with most adequacy. The precision is determined by contrasting the anticipated result and ongoing result of the patient. In light of thinking about different boundaries, the model will anticipate the danger of a patient of his endurance rate of acute or chronic infected person accuracy. In this paper we use stochastic gradient algorithm to find the Co-connection between boundaries of the date set, kernel approximation to finalise the resulting accuracy of the acute or choric prediction of patients and SVM method we use to clustering the kernel approximation calculation and connection analysis.

关键词： Hepatitis B Machine learning SVM stochastic gradient algorithm Dataset

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Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

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IET SIGNAL PROCESSING 2020年第7期14卷 455-466页

作者： Pan, Jian Ma, Hao Zhang, Xiao Liu, Qinyao Ding, Feng Chang, Yufang Sheng, Jie Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Univ Washington Sch Engn & Technol Tacoma WA 98402 USA

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input-output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.

关键词： identification parameter estimation least squares approximations gradient methods autoregressive moving average processes state-space methods stochastic processes multivariable control systems recursive coupled projection algorithms multivariable output-error-like system coloured noises coupling identification concept gradient search projection algorithm stochastic gradient algorithm autoregressive moving average noise input-output data identification model hierarchical identification principle parameter estimation algorithm coupled relationship

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Identifying Cognitive Radars-Inverse Reinforcement Learning Using Revealed Preferences

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 4529-4542页

作者： Krishnamurthy, Vikram Angley, Daniel Evans, Robin Moran, Bill Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

We consider an inverse reinforcement learning problem involving "us" versus an "enemy" radar equipped with a Bayesian tracker. By observing the emissions of the enemy radar, how can we identify if the radar is cognitive (constrained utility maximizer)? Given the observed sequence of actions taken by the enemy's radar, we consider three problems: (i) Are the enemy radar's actions (waveform choice, beam scheduling) consistent with constrained utility maximization? If so how can we estimate the cognitive radar's utility function that is consistent with its actions. We formulate, and solve the problem in terms of the spectra (eigenvalues) of the state, and observation noise covariance matrices, and the algebraic Riccati equation. (ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar's actions in noise or the radar observes our probe signal in noise? We propose a statistical detector with a tight Type-II error bound. (iii) How can we optimally probe (interrogate) the enemy's radar by choosing our state to minimize the Type-II error of detecting if the radar is deploying an economic rational strategy, subject to a constraint on the Type-I detection error? We present a stochastic optimization algorithm to optimize our probe signal. The main analysis framework used in this paper is that of revealed preferences from microeconomics.

关键词： Probes Radar detection Cognitive radar Radar tracking Learning (artificial intelligence) Bayes methods Revealed preferences inverse reinforcement learning adversarial signal processing identifying cognitive behavior spectral revealed preferences Afriat's theorem stochastic gradient algorithm detection economics-based-rationality Kalman filter tracker algebraic Riccati equation waveform selection beam scheduling

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Maximum likelihood identification of dual-rate Hammerstein output-error moving average system

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IET CONTROL THEORY AND APPLICATIONS 2020年第8期14卷 1078-1090页

作者： Li, Junhong Zhang, Jiali Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This study discusses the parameter estimation of the Hammerstein output-error moving average system using the dual-rate sampled data. The polynomial transformation technique is used to obtain the identification model of the discussed dual-rate sampled systems. The stochastic gradient optimisation method is an effective optimisation method. Compared with the Newton optimisation, it only needs to calculate the first derivative during the optimisation and the amount of calculation is relatively small. It is a good choice to use the stochastic gradient algorithm for the identification of Hammerstein dual-rate model after using the polynomial transformation technique. In order to improve the convergence speed, a maximum likelihood forgetting factor stochastic gradient identification algorithm is proposed by combining the maximum likelihood principle and the gradient search method. The convergence of the algorithm is analysed by using the stochastic process theory. Furthermore, in order to improve the estimation accuracy of the identification algorithm, a maximum likelihood multi-innovation forgetting factor stochastic gradient algorithm is proposed by using the multi-innovation identification theory. The effectiveness of the proposed algorithms is illustrated by a numerical simulation example and a water tank system.

关键词： parameter estimation least squares approximations stochastic processes maximum likelihood estimation optimisation polynomials gradient methods maximum likelihood identification Hammerstein output-error moving average system dual-rate sampled data polynomial transformation technique identification model dual-rate sampled systems stochastic gradient optimisation method Newton optimisation stochastic gradient algorithm Hammerstein dual-rate model maximum likelihood principle gradient search method stochastic process theory water tank system multiple innovation identification theory maximum likelihood multi-innovation forgetting factor stochastic gradient identification algorithm

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Global Mapping Analysis: stochastic gradient algorithm in Multidimensional Scaling

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2012年第2期E95D卷 596-603页

作者： Matsuda, Yoshitatsu Yamaguchi, Kazunori Aoyama Gakuin Univ Sagamihara Kanagawa 2525258 Japan Univ Tokyo Tokyo 1538902 Japan

In order to implement multidimensional scaling (MDS) efficiently, we propose a new method named "global mapping analysis" (GMA), which applies stochastic approximation to minimizing MDS criteria. GMA can solve MDS more efficiently in both the linear case (classical MDS) and non-linear one (e.g., ALSCAL) if only the MDS criteria are polynomial. GMA separates the polynomial criteria into the local factors and the global ones. Because the global factors need to be calculated only once in each iteration, GMA is of linear order in the number of objects. Numerical experiments on artificial data verify the efficiency of GMA. It is also shown that GMA can find out various interesting structures from massive document collections.

关键词： multidimensional scaling stochastic gradient algorithm text mining

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