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fast recursive algorithm for two-dimensional Tsallis entropy thresholding method

Journal of Systems Engineering and Electronics 2009年第3期20卷 619-624页

作者： Tang Yinggan Di Qiuyan Guan Xinping Inst. of Electrical Engineering Yanshan Univ. Qinhuangdao 066004 P. R. China

Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.

关键词： image segmentation thresholding Tsallis entropy fast recursive algorithm

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An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on fast recursive algorithm: With Application to a Linear Motor

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第4期19卷 6160-6169页

作者： Fu, Xuewei Yang, Xiaofeng Zanchetta, Pericle Tang, Mi Liu, Yang Chen, Zhenyu Fudan Univ Sch Microelect State Key Lab ASIC & Syst Shanghai 200433 Peoples R China Univ Nottingham Dept Elect & Elect Engn Nottingham NG7 2RD England Univ Pavia Dept Elect Comp & Biomed Engn I-27100 Pavia Italy Univ Nottingham Power Elect Machine & Control Grp Nottingham NG7 2RD England Harbin Inst Technol Ctr Ultra Precis Optoelect Instrument Engn Harbin 150001 Peoples R China

The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on a fast recursive algorithm (IFFT-FRA) is developed in this article. Explicitly, based on FRA, the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in the presence of noise. Comparative experiments on a linear motor confirm the effectiveness of the proposed approach.

关键词： Feedforward systems Tuning Iterative methods Estimation Instruments Task analysis Informatics Data-based control data driven fast recursive algorithm iterative feedforward tuning (IFFT) linear motor

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fast recursive algorithm for two-dimensional Tsallis entropy thresholding method

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Chinese Journal of Systems Engineering and Electronics 2009年第3期000卷

作者： Tang Yinggan Di Qiuyan Guan Xinping nst. of Electrical Engineering Yanshan Univ. Qinhuangdao 066004 P. R. China

Recently, a two-dimensional (2-D) Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O(L2) from O(L4). The effectiveness of the proposed algorithm is illustrated by experimental results.

关键词： image segmentation thresholding Tsallis entropy fast recursive algorithm

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On fast recursive algorithms for V-BLAST with Optimal Ordered SIC Detection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2009年第6期8卷 2860-2865页

作者： Shang, Yue Xia, Xiang-Gen Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two improved algorithms have been also recently introduced by Szczecinski-Massicotte and Zhu-Lei-Chin independently. In this letter, our first contribution is to incorporate the existing improvements into the original fast recursive algorithm to provide the fastest known algorithm for the optimal ordered SIC detection of V-BLAST. Subsequently, we propose two new algorithms that result from one further improvement for the fast recursive algorithm. Compared with the fastest known algorithm built from the existing improvements, one new proposed algorithm has a speedup of 1.3 times in both the number of multiplications and the number of additions, and the other new proposed algorithm requires less intermediate variables and saves memories.

关键词： fast recursive algorithm MMSE successive interference cancellation V-BLAST ZF

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A fast nonlinear model identification method

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2005年第8期50卷 1211-1216页

作者： Li, K Peng, JX Irwin, GW Queens Univ Belfast Sch Elect & Elect Engn Belfast BT9 5AH Antrim North Ireland

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

关键词： computational complexity fast recursive algorithm nonlinear system identification numerical stability

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A fast model identification method for networked control system

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APPLIED MATHEMATICS AND COMPUTATION 2008年第2期205卷 658-667页

作者： Fei, Minrui Du, Dajun Li, Kang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland

Most control strategies for networked control system (NCS) assume that system model is known as 'a priori', which are however impractical in many industrial applications. To obtain a suitable model for networked control, this paper concentrates on model identification under the networked control environment. A networked identification scheme is proposed here, and nondeterministic factors such as network-induced delay, data packet out-of-order, and data packet loss between the sensor and identifier as well as the identifier and the actuator are considered. A discard-packet strategy is first developed to deal with network nondeterministic factors from the sensor to the identifier, and a cubic spline interpolation is used to compensate lost data. Actuator buffers are then introduced for actuator nodes to handle network nondeterministic factors between the identifier and the actuator. Finally, a fast recursive algorithm is used both to select the model structure and to estimate the unknown system parameters. Experiments on ARMA model identification and NARMAX model identification are performed respectively under different network loads. Simulation results show that the proposed networked identification scheme can effectively overcome the impact of various network-induced nondeterministic factors and significantly improve model identification performance. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Networked control system Network-induced delay fast recursive algorithm Cubic spline

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Two-Stage Method for Diagonal Recurrent Neural Network Identification of a High-Power Continuous Microwave Heating System

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NEURAL PROCESSING LETTERS 2019年第3期50卷 2161-2182页

作者： Liu, Tong Liang, Shan Xiong, Qingyu Wang, Kai Chongqing Univ MOE Minist Educ Key Lab Complex Syst Safety & Control Chongqing 400044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Chongqing Univ Sch Software Engn Chongqing 400044 Peoples R China

This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design involves a two-stage training process that couples an efficient forward model selection technique with gradient-based optimization. In the first stage, an impact recurrent network structure is obtained by a fast recursive algorithm in a stepwise forward procedure. To ensure stability, update rules are further developed using Lyapunov stability criterion to tune parameters of reduced size model at the second stage. The proposed approach is tested with an experimental regression problem and a practical HPCMHS identification, and the results are compared with four typical network models. The results show that the new design demonstrates improved accuracy and model compactness with reduced computational complexity over the existing methods.

关键词： Diagonal recurrent neural network High-power continuous microwave heating system fast recursive algorithm Lyapunov stability criterion Computational complexity

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Improved nonlinear PCA for process monitoring using support vector data description

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JOURNAL OF PROCESS CONTROL 2011年第9期21卷 1306-1317页

作者： Liu, Xueqin Li, Kang McAfee, Marion Irwin, George W. Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Intelligent Syst & Control Grp Belfast BT9 5AH Antrim North Ireland Inst Technol Sligo Dept Mech & Elect Engn Sligo Ireland

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the fast recursive algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Nonlinear principal component analysis Support vector datadescription fast recursive algorithm Principal curves Radial basis function network

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Thermal monitoring of lithium-ion batteries based on machine learning and fibre Bragg grating sensors

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第8期45卷 1570-1578页

作者： Liu, Shiyun Li, Kang Univ Leeds Sch Elect & Elect Engn Leeds England Univ Leeds Sch Elect & Elect Engn Woodhouse Lane Leeds LS2 9JT England

Lithium-ion batteries (LiBs) are well-known power sources due to their higher power and energy densities, longer cycle life and lower self-discharge rate features. Hence, these batteries have been widely used in various portable electronic devices, electric vehicles and energy storage systems. The primary challenge in applying a Lithium-ion battery (LiB) system is to guarantee its operation safety under both normal and abnormal operating conditions. To achieve this, temperature management of batteries should be placed as a priority for the purpose of achieving better lifetime performance and preventing thermal failures. In this paper, fibre Bragg Grating (FBG) sensor technology coupling with machine learning (ML) has been explored for battery temperature monitoring. The results based on linear and nonlinear models have confirmed that the novel methods can estimate temperature variations reliably and accurately.

关键词： Lithium-ion battery thermal management FBG sensor fast recursive algorithm linear nonlinear model

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Two-stage extreme learning machine for regression

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NEUROCOMPUTING 2010年第16-18期73卷 3028-3038页

作者： Lan, Yuan Soh, Yeng Chai Huang, Guang-Bin Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It proved to be very fast and effective especially for solving function approximation problems with a predetermined network structure. However, the method for determining the network structure of preliminary ELM may be tedious and may not lead to a parsimonious solution. In this paper, a systematic two-stage algorithm (named TS-ELM) is introduced to handle the problem. In the first stage, a forward recursive algorithm is applied to select the hidden nodes from the candidates randomly generated in each step and add them to the network until the stopping criterion achieves its minimum. The significance of each hidden node is then reviewed in the second stage and the insignificance ones are removed from the network, which drastically reduces the network complexity. The effectiveness of TS-ELM is verified by the empirical studies in this paper. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Extreme learning machine fast recursive algorithm Final prediction error Leave-one-out cross-validation

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