For many complex industrial applications, traditional attribute reduction algorithms are often inefficient in obtaining optimal reducts that align with mechanistic analyses and practical production requirements. To so...
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For many complex industrial applications, traditional attribute reduction algorithms are often inefficient in obtaining optimal reducts that align with mechanistic analyses and practical production requirements. To solve this problem, we propose a recursive attribute reduction algorithm that calculates the optimal reduct. First, we present the notion of priority sequence to describe the background meaning of attributes and evaluate the optimal reduct. Next, we define a necessary element set to identify the "individually necessary" characteristics of the attributes. On this basis, a recursive algorithm is proposed to calculate the optimal reduct. Its boundary logic is guided by the conflict between the necessary element set and the core attribute set. The experiments demonstrate the proposed algorithm's uniqueness and its ability to enhance the prediction accuracy of the hot metal silicon content in blast furnaces.
Frequency-tunable lasers allow the removal of phase ambiguities in interferometric profilometry through the synthetic wavelength longer than height variations in the sample. The subsequent measurements lowering synthe...
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Frequency-tunable lasers allow the removal of phase ambiguities in interferometric profilometry through the synthetic wavelength longer than height variations in the sample. The subsequent measurements lowering synthetic wavelengths and updating phase difference values improves the measurement. The surface profilometry on samples with tilted interference can lead to an initial synthetic phase map, though locally unambiguous, getting globally wrapped whose unwrapping can be difficult in the presence of noise. Starting with the synthetic phase map that is locally unambiguous but globally wrapped can still update the phase values through a recursive algorithm. The artefact of 2 pi jumps in the initial wrapped synthetic phase just gets carried forward which can be easily removed from the final phase map obtained with the shortest possible synthetic wavelength. Choosing a point on the sample surface as the point of comparison, the proposed interferometry scheme can be made immune to surrounding vibrations while tuning wavelengths.
Due to the influence of various factors on bridge sensors, the signals obtained often contain multiple signal components, including temperature and vehicle induced effect. It is necessary to separate and analyze indiv...
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Due to the influence of various factors on bridge sensors, the signals obtained often contain multiple signal components, including temperature and vehicle induced effect. It is necessary to separate and analyze individual signals in bridge health detection. In order to separate temperature and vehicle response components from complex signals, this article proposes an improved variational mode decomposition (VMD) algorithm based on recursive methods, which takes the mean value of each recursive block as the eigenvalue, fits the eigenvalues of each recursive block using the least squares method, and separates the first intrinsic mode function. The applicability of this method in the field of bridges was first verified through modal decomposition of simulated deflection and strain data. Then based on the health monitoring data of the Jingtai Expressway viaduct, the rapid separation of temperature response and vehicle response of the bridge has been achieved. The results indicate that the recursive method, in an online continuous decomposition environment, is approximately seven times faster than the traditional VMD algorithm. Moreover, when setting the same penalty factor, the mean square error obtained from separating finite element simulation data is smaller than that of VMD, and the separated actual measurement data has a higher correlation coefficient with temperature. This resolves the computational speed issue of the VMD algorithm in real-time bridge health monitoring, demonstrating the feasibility of the recursive algorithm, and effectively separates signals related to temperature and vehicles.
Adequate description of phosphorus processes and high computational demand are core challenges in watershed modeling. In this study, a distributed model was developed, including rainfall-runoff module, erosion module,...
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Adequate description of phosphorus processes and high computational demand are core challenges in watershed modeling. In this study, a distributed model was developed, including rainfall-runoff module, erosion module, soil enrichment-loss module, watershed confluence-dissipation module, and river transmission module. To improve the efficiency of the distributed model, the recursive algorithms and Hash Tables are used. Besides, the Colloidal Phosphorus (CP) module was also added as its fate differs from Dissolved P (DP) and Particulate P (PP). The erosion module and soil P enrichment and loss module are modified based on the loss equations of colloids and CP with pH. The case stuty in the Three Gorges Reservoir area of China shows that the R2 and NSE of the distributed model are greater than 0.4, indicating a good model performance. Compared with traditional models, the developed model has been improved in terms of computational efficiency and P process description. Compared with forward confluence method, the running time of new model is reduced from 6 s to 0.5 s. Also its calibration time was 66 ' 4 '', 40 ' 8 '' and 25 ' 3 '' at a grid scale of 30 m, 40 m and 50 m, respectively. Compared to conventional two-phase model that can misestimate P loss, the new model can provide reasonable results. The developed model provides a methodological basis for precise simulation and prevention of non-point source pollution.
This paper studies a parameter estimation problem of networked linear systems with fixed-rate quantization. Under the minimum mean square error criterion, we propose a recursive estimator of stochastic approximation t...
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This paper studies a parameter estimation problem of networked linear systems with fixed-rate quantization. Under the minimum mean square error criterion, we propose a recursive estimator of stochastic approximation type, and derive a necessary and sufficient condition for its asymptotic unbiasedness. This motivates to design an adaptive quantizer for the estimator whose strong consistency, asymptotic unbiasedness, and asymptotic normality are rigorously proved. Using the Newton-based and averaging techniques, we obtain two accelerated recursive estimators with the fastest convergence speed of O(1/k), and exactly evaluate the quantization effect on the estimation accuracy. If the observation noise is Gaussian, an optimal quantizer and the accelerated estimators are co-designed to asymptotically approach the minimum Cramer-Rao lower bound. All the estimators share almost the same computational complexity as the gradient algorithms with un-quantized observations, and can be easily implemented. Finally, the theoretical results are validated by simulations. (C) 2014 Elsevier Ltd. All rights reserved.
This work develops stochastic optimization algorithms for a class of stock liquidation problems. The stock liquidation rules are based on hybrid geometric Brownian motion models allowing regime changes that are modula...
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This work develops stochastic optimization algorithms for a class of stock liquidation problems. The stock liquidation rules are based on hybrid geometric Brownian motion models allowing regime changes that are modulated by a continuous-time finite-state Markov chain. The optimal selling policy is of threshold type and can be obtained by solving a set of two-point boundary value problems. The total number of equations to be solved is the same as the number of states of the underlying Markov chain. To reduce the computational burden, using a stochastic optimization approach, recursive algorithms are constructed to approximate the optimal threshold values. Convergence and rates of convergence of the algorithm are studied. Simulation examples are presented, and the computation results are compared with the analytic solutions. Finally, the algorithms are tested using real market data.
A unified presentation of recursive algorithms for plant model identification in closed loop is given. From the basic formulation of the problem of finding the plant model which gives the best predictor for the closed...
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ISBN:
(纸本)0780335910
A unified presentation of recursive algorithms for plant model identification in closed loop is given. From the basic formulation of the problem of finding the plant model which gives the best predictor for the closed loop system the two families of algorithms using either a re-parameterized predictor for the closed loop or a plant predictor operating on filtered data are presented and their asymptotic properties are examined. Validation tests for the models identified in closed loop are proposed.
This paper describes a recursive algorithm suitable for microprocessor based power system relaying and measurement applications. The algorithm is designed using the least error squares curve fitting technique. The mat...
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This paper describes a recursive algorithm suitable for microprocessor based power system relaying and measurement applications. The algorithm is designed using the least error squares curve fitting technique. The mathematical background for the non-recursive least error squares algorithm is extended to form a recursive algorithm. A method for including decaying dc and harmonic frequencies in the algorithm is described. Finally, sample studies are presented to demonstrate the performance of the developed algorithm.
This paper develops an efficient, recursive algorithm for determining the economic power dispatch of thermal generators within the unit commitment environment. A method for incorporating the operation limits of all on...
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This paper develops an efficient, recursive algorithm for determining the economic power dispatch of thermal generators within the unit commitment environment. A method for incorporating the operation limits of all on-line generators and limits due to ramping generators is developed in the paper. The developed algorithm is amenable for computer implementation using the artificial intelligence programming language, Prolog. The performance of the developed algorithm is demonstrated through its application to evaluate the costs of dispatching 13 thermal generators within a generator schedule in a 24-hour schedule horizon.
An improved recursive Levenberg-Marquardt algorithm (RLM) is proposed to more efficiently train neural networks. The error criterion of the RLM algorithm was modified to reduce the impact of the forgetting factor on t...
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An improved recursive Levenberg-Marquardt algorithm (RLM) is proposed to more efficiently train neural networks. The error criterion of the RLM algorithm was modified to reduce the impact of the forgetting factor on the convergence of the algorithm. The remedy to apply the matrix inversion lemma in the RLM algorithm was extended from one row to multiple rows to improve the success rate of the convergence;after that, the adjustment strategy was modified based on the extended remedy. Finally, the performance of this algorithm was tested on two chaotic systems. The results show improved convergence. (C) 2017 Elsevier Ltd. All rights reserved.
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