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parameter learning for Wiener systems with time-delay state-space model

ASIAN JOURNAL OF CONTROL 2025年第1期27卷 179-190页

作者： Li, Feng Ding, Zhenyu He, Naibao Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Jiangsu Peoples R China

This paper discusses a novel scheme for learning the Wiener output error nonlinear system with time-delay state-space model. In the Wiener system, the dynamic linear block is approximated by time-delay state-space model, and the static nonlinear block is established using neural fuzzy network. Combined signals designed including separable signal and random signal are devoted to achieving parameters separation learning of the Wiener system, that is, the two blocks are learned independently. Firstly, using the properties of shift operator and transforming state-space model with time-delay into a representation with input and output, then linear dynamic block parameters are learned by the virtue of correlation analysis method in the condition of Gaussian signals. Moreover, a recursive extended least squares estimation is carried out to learn parameters of static nonlinear block and colored noise model under the condition of random signals. The efficiency and accuracy of proposed scheme are confirmed on experiment results of a numerical simulation and a typical practical nonlinear process, and experimental simulation results demonstrate that the learning scheme proposed obtains good learning precision.

关键词： combined signals neural fuzzy network parameter learning state-space model Wiener nonlinear system

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Physics-informed neural networks for parameter learning of wildfire spreading

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 434卷

作者： Vogiatzoglou, K. Papadimitriou, C. Bontozoglou, V. Ampountolas, K. Univ Thessaly Dept Mech Engn Syst Dynam Lab Volos 38334 Greece Univ Thessaly Dept Mech Engn Transport Proc & Proc Equipment Lab Volos 38334 Greece Univ Thessaly Dept Mech Engn Automat Control & Autonomous Syst Lab Volos 38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data- driven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies.

关键词： Wildfire spreading Physical model parameter learning Deep learning Physics-informed neural network Physics-informed digital twin

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parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor

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KNOWLEDGE-BASED SYSTEMS 2015年 73卷 69-80页

作者： Chang, Leilei Sun, Jianbin Jiang, Jiang Li, Mengjun High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

The Inertial Confinement Fusion (ICF) laser device consists of thousands of Metalized Film Capacitors (MFC). The Belief Rule Base (BRB) system has shown privileges in reflecting complex system dynamics. However, the BRB system requires the referenced values of each attribute to be limited. The traditional BRB learning and training approaches are no longer applicable since the referenced values of the attributes in the BRB system are pre-determined. A parameter learning approach is proposed with three strategies and each strategy is designed for one specific scenario. Strategy I (for Scenario I) is designed when only the training dataset is selectable. Strategy II (for Scenario II) is designed when new referenced values are predictable yet there is only one scale in the conclusion part. Strategy HI (for Scenario III) is designed when new referenced values are predictable and there are multiple scales in the conclusion part. The Differential Evolution (DE) algorithm is used as the optimization engine to identify the key referenced values. A case is studied to validate the efficiency of the proposed parameter learning approach with multiple referenced values. The comparative results show that the parameter learning approach performs best in Scenario III. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Belief rule base parameter learning Differential evolution Residual life probability prediction Metalized film capacitor

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parameter learning of stochastic Boolean networks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第5期32卷 2472-2484页

作者： Chen, Hongwei Shen, Bo Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Res Ctr Digitalized Text & Fash Technol Minist Educ Shanghai Peoples R China

In this article, the parameter learning problem is studied for stochastic Boolean networks (SBNs). Both the measure noise and the system noise are assumed to be white and modeled by sequences of Bernoulli distributed stochastic variables which are mutually independent. An algebraic representation of the SBNs is obtained by taking advantage of vector expression of logic variable and applying the semi-tensor product technique. Consequently, the parameter learning problem is reformulated as an optimization problem that makes it possible to identify the system matrices of SBNs in an efficient computation way. Subsequently, properties of forward and backward probabilities are investigated, and the EM algorithm is utilized to learn the model parameters from time series data. Finally, a numerical experiment is presented to show the usefulness of the designed parameter learning algorithm.

关键词： EM algorithm parameter learning semi-tensor product stochastic Boolean networks

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parameter learning for the Nonlinear System Described by a Class of Hammerstein Models

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第5期42卷 2635-2653页

作者： Li, Feng Zhu, Xinjian Cao, Qingfeng Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225127 Peoples R China

A novel parameter learning scheme using multi-signals is proposed for estimating parameters of the Hammerstein nonlinear model in this research. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the designed multi-signals that composed of separable signals and random signals are used for learning separately the nonlinear block parameters and the linear block parameters. Firstly, the linear block parameters are learned by virtue of correlation analysis algorithm based on input-output data of separable signals, which effectively deals with the unmeasurable variable of the Hammerstein model. In addition, auxiliary error-based probability density function strategy is employed to estimate the nonlinear block parameters with the help of random signals, which makes the model error present normal distribution, as well as improves model accuracy effectively. Studies with a numerical simulation and a typical industrial process demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the accuracy and efficiency of the developed parameter learning scheme.

关键词： Hammerstein nonlinear model Correlation analysis Probability density function parameter learning

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parameter learning and applications of the inclusion-exclusion integral for data fusion and analysis

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INFORMATION FUSION 2020年 56卷 28-38页

作者： Honda, Aoi James, Simon Deakin Univ Sch Informat Technol Geelong Vic Australia

Developments in the learning and interpretation of fuzzy integrals have paved the way for a myriad of applications in data analysis and prediction. The ability of the associated fuzzy measure to model heterogeneous interactions allow high flexibility when it comes to data fusion tasks - comparable to that of neural networks - however the fuzzy integral structure and properties also afford a degree of robustness and interpretability not enjoyed by such tools. On the other hand, neural network architectures can accommodate fuzzy integrals as a special case. In this paper, we propose that such a representation allows us to naturally extend and adapt the fuzzy integral framework toward specific applications. We focus on the inclusion-exclusion integral, which is a generalization of the Choquet integral, and detail methods for learning the various parameters, given its extended architecture. We then validate the performance and usefulness of this approach on some benchmark datasets.

关键词： Fuzzy integral Aggregation function Inclusion-exclusion integral Fuzzy measure Weight learning parameter learning Neural networks

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parameter learning of personalized trust models in broker-based distributed trust management

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INFORMATION SYSTEMS FRONTIERS 2006年第4期8卷 321-333页

作者： Hsu, Jane Yung-jen Lin, Kwei-Jay Chang, Tsung-Hsiang Ho, Chien-ju Huang, Han-Shen Jih, Wan-rong Natl Taiwan Univ Taipei 106 Taiwan Univ Calif Irvine Irvine CA 92697 USA Acad Sinica Inst Informat Sci Taipei 115 Taiwan

Distributed trust management addresses the challenges of eliciting, evaluating and propagating trust for service providers on the distributed network. By delegating trust management to brokers, individual users can share their feedbacks for services without the overhead of maintaining their own ratings. This research proposes a two-tier trust hierarchy, in which a user relies on her broker to provide reputation rating about any service provider, while brokers leverage their connected partners in aggregating the reputation of unfamiliar service providers. Each broker collects feedbacks from its users on past transactions. To accommodate individual differences, personalized trust is modeled with a Bayesian network. Training strategies such as the expectation maximization (EM) algorithm can be deployed to estimate both server reputation and user bias. This paper presents the design and implementation of a distributed trust simulator, which supports experiments under different configurations. In addition, we have conducted experiments to show the following. 1) Personal rating error converges to below 5% consistently within 10,000 transactions regardless of the training strategy or bias distribution. 2) The choice of trust model has a significant impact on the performance of reputation prediction. 3) The two-tier trust framework scales well to distributed environments. In summary, parameter learning of trust models in the broker-based framework enables both aggregation of feedbacks and personalized reputation prediction.

关键词： distributed trust management reputation mechanism probabilistic trust model personalized feedback rating parameter learning expectation maximization

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parameter learning for an Intuitionistic Fuzzy Belief Rule-Based Systems Based on Weight and Reliability

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第2期23卷 219-228页

作者： Wang, Yanni China Elect Standardizat Inst 1 Andingmen East St Beijing 100007 Peoples R China Beihang Univ 37 Xueyuan Rd Beijing 100083 Peoples R China

The intent of the parameter learning is to ensure the accuracy of intuitionistic fuzzy belief rule-based systems (IFBRBSs) considering both weight and reliability. The main contribution is that distinguish reliability and weight respectively treated as intrinsic and extrinsic properties of evidence. A parameter learning method considering both reliability and weight determined by internal and external conflicts (PL-RWIEC) is proposed. Evidence reasoning with reliability and weight is introduced as a basis of the learning process. After learning, the mean square error (MSE) between the real output and the simulated output decreases 75 times. Compared to the parameter learning considering both reliability and weight determined by Dempster's conflict (PL-RW-DC) and compared to the parameter learning not considered reliability (PL-NR), the PL-RW-IEC method gets the most accurate result according to the MSE.

关键词： fuzzy belief reliability weight parameter learning

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parameter learning for the nonlinear system described by Hammerstein model with output disturbance

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ASIAN JOURNAL OF CONTROL 2023年第2期25卷 886-898页

作者： Li, Feng Zhu, Xinjian He, Naibao Gu, Ya Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai Peoples R China

A novel parameter learning scheme using multi-signal processing is developed that aims at estimating parameters of the Hammerstein nonlinear model with output disturbance in this paper. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the multi-signals are devised to estimate separately the nonlinear block parameters and the linear block parameters;the parameter estimation procedure is greatly simplified. Firstly, in view of the input-output data of separable signals, the linear block parameters are computed through correlation analysis method, thereby the influence of output noise is effectively handled. In addition, model error probability density function technology is employed to estimate the nonlinear block parameters with the help of measurable input-output data of random signals, which not only controls the space state distribution of model error but also makes error distribution tends to normal distribution. The simulation results demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the effectiveness of the developed approach.

关键词： correlation analysis Hammerstein nonlinear model parameter learning probability density function

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parameter learning in hybrid Bayesian networks using prior knowledge

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DATA MINING AND KNOWLEDGE DISCOVERY 2016年第3期30卷 576-604页

作者： Perez-Bernabe, Inmaculada Fernandez, Antonio Rumi, Rafael Salmeron, Antonio Univ Almeria Dept Math Almeria 04120 Spain

Mixtures of truncated basis functions have been recently proposed as a generalisation of mixtures of truncated exponentials and mixtures of polynomials for modelling univariate and conditional distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning the parameters of marginal and conditional MoTBF densities when both prior knowledge and data are available. Incorporating prior knowledge provide a valuable tool for obtaining useful models, especially in domains of applications where data are costly or scarce, and prior knowledge is available from practitioners. We explore scenarios where the prior knowledge can be expressed as an MoTBF density that is afterwards combined with another MoTBF density estimated from the available data. The resulting model remains within the MoTBF class which is a convenient property from the point of view of inference in hybrid Bayesian networks. The performance of the proposed method is tested in a series of experiments carried out over synthetic and real data.

关键词： Hybrid Bayesian networks Mixtures of truncated basis functions parameter learning Prior information

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