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Design of Chinese Corpus Based on Semantic Mining Algorithm

ADVANCES IN MULTIMEDIA 2022年第0期2022卷

作者： Wu, Di Henan Polytech Zhengzhou 450000 Peoples R China

In order to improve the practical effect of the Chinese corpus, this paper combines the semantic mining algorithm to design the Chinese corpus, proposes an ontology adaptive algorithm based on content learning, and conducts in-depth research on the model of the algorithm. Firstly, in view of the heterogeneity of web information structure and the chaotic nature of information organization, this paper proposes a web content extraction method to effectively remove noise information. Moreover, this paper analyzes an ontology-based text content parsing method, which uses the semantic parsing capability of the ontology to improve the semantic parsing capability of the text. Secondly, this paper proposes the BFA algorithm to optimize the BP neural network. The experimental research shows that the Chinese corpus system based on semantic mining proposed in this paper has a good practical effect and meets the actual needs of Chinese teaching and translation.

关键词： adaptive algorithms

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Research Progress on Intelligent System's Learning, Optimization, and Control-Part II: Online Sparse Kernel adaptive Algorithm

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第12期50卷 5369-5385页

作者： Tan, Fuxiao Guan, Xinping Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

When dealing with complex nonlinear signals in intelligent system, by defining the inner product of two data vectors in the feature space, the kernel function can reflect the nonlinear mapping relationship between reproducing kernel Hilbert space (RKHS) and original data space. Therefore, by implementing classical linear adaptive filtering in RKHS space, the filtering operation can be expressed as a special relation of inner product with kernel function, which is referred to as "kernel trick." As long as these algorithms can be expressed in the form of inner product, not only the convex least squares problem can be solved iteratively, but also the nonlinear adaptive filtering algorithms can be obtained, which have both general approximation characteristics and convexity. Therefore, the combination of kernel method and adaptive filtering algorithm is realized. On the other hand, since the Gram matrix is used, the dimension of kernel adaptive algorithm is determined by the number of data samples. When the number of observation sample point is increasing, the size of the state space increases exponentially with the growth of the dimension. Thereby, the kernel adaptive algorithm should solve the problem of online sparsification to avoid "curse of dimensionality." As part II, based on online sparse kernel learning and the classical adaptive filtering algorithm, the kernel adaptive algorithms and online sparse algorithms are investigated in this paper. The main works of this paper have two aspects. First, combined with classical adaptive algorithm and kernel feature mapping, this paper investigates the basic concept of kernel adaptive algorithm and the realization mechanism of four kernel adaptive algorithm intensively: 1) kernel least mean squares;2) kernel recursive least squares;3) kernel affine projection algorithm;and 4) kernel principal component analysis. Second, in order to reduce the computational complexity, this paper studies some online sparse algorithms which

关键词： Kernel adaptive algorithms Signal processing algorithms Intelligent systems Symmetric matrices Approximation algorithms Computational complexity adaptive filtering linear estimation online kernel learning online sparsification algorithm reproducing kernel Hilbert space (RKHS)

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adaptive hedging under delayed feedback

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NEUROCOMPUTING 2020年 397卷 356-368页

作者： Korotin, Alexander V'yugin, Vladimir Burnaev, Evgeny Skolkovo Inst Sci & Technol Moscow Moscow Oblast Russia

The article is devoted to investigating the application of hedging strategies to online expert weight allocation under delayed feedback. As the main result we develop the General Hedging algorithm G based on the exponential reweighing of experts' losses. We build the artificial probabilistic framework and use it to prove the adversarial loss bounds for the algorithm G in the delayed feedback setting. The designed algorithm G can be applied to both countable and continuous sets of experts. We also show how algorithm G extends classical Hedge (Multiplicative Weights) and adaptive Fixed Share algorithms to the delayed feedback and derive their regret bounds for the delayed setting by using our main result. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hedging Decision-theoretic online learning Experts problem Delayed feedback adaptive algorithms Non-replicating algorithms Adversarial setting

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adaptive Algorithm for Sparse System Identification Based on Hard-Thresholding Techniques

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3597-3601页

作者： Lee, Han-Sol Lee, Jae-Woo Song, Woo-Jin Kim, Seong-Eun Samsung Elect Syst LSI Business Hwasung 18448 South Korea Pohang Univ Sci & Technol Dept Elect & Elect Engn Pohang 37673 South Korea Hanbat Natl Univ Dept Elect & Control Engn Daejeon 34158 South Korea

Sparse least mean square (LMS) algorithms employ approximations of sparseness constraints as a zero-point attraction term that forces small tap weights towards the origin when unknown systems to be identified are sparse. Recently, the online linearized Bregman iteration (OLBI) algorithm appreciated soft thresholding techniques based on an L-1-norm regularization in reducing a steady-state error. Although the soft thresholding successfully improves accuracy of the adaptive filter for sparse systems, this brief is limited to the L-1-norm regularization. In sparse representation, the L-0-norm regularization can theoretically yield the sparsest representation and lead to the promising performance in adaptive filters. In this regard, we introduce a L-0-norm based LMS algorithm by exploiting a hard thresholding through a variable splitting method. The proposed algorithm preserves the behavior of large tap weights and strongly enforces small tap weights to zero by relaxation of L-0-norm regularization. We also provide the mean stability conditions and theoretical mean-square performance of the proposed algorithm. Experimental results show that the proposed algorithm achieves superior convergence performance compared with conventional sparse algorithms.

关键词： Approximation algorithms Steady-state Convergence Circuits and systems adaptive algorithms Circuit stability Stability analysis adaptive filter least mean square algorithm sparse system identification sparseness constraint hard thresholding

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Enterprise Management Model Innovation and Performance Index Evaluation Based on the Era of Big Data

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Wireless Communications and Mobile Computing 2022年第0期2022卷

作者： Xie, Yun Huaxin Gas Group Co. Ltd. Shanxi Taiyuan030000 China

The innovation of enterprise management model has increasingly become one of the important sources of competitive advantage for enterprises. At present, the market environment is changing rapidly, and information technology is changing the nature and structure of enterprise competition. In the competitive landscape of enterprises under the background of network economy, only relying on technological innovation can no longer meet the needs of enterprise development. In recent years, the successful practice of a large number of enterprises has shown that the innovation of enterprise management mode is also an indispensable and important factor to promote the sustainable development of enterprises. In the new format of competition among enterprises, the innovation of enterprise management mode is playing an important role. Scholars have very rich research contents on the innovation of enterprise management model. First, they focus on the research between the innovation of enterprise management model and the value creation of enterprises. They believe that determining the structure of new business and innovation of enterprise management model can create value for enterprises. The innovation of enterprise management model can effectively explain the differences in enterprise performance;secondly, the research of enterprise management model innovation has been extended to its classification research;that is, different types of enterprise management model innovation have different effects on performance;finally, in the existing research, in this paper, some progress has been made in the exploration of the mediating variables between enterprise management model innovation and performance. The example verification shows that the performance of the momentum-adaptive algorithm is the best. Using the BP network model can effectively evaluate the innovation of enterprise management mode and performance indicators, so as to guide the implementation of enterprise management mode i

关键词： adaptive algorithms

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Assessment of adaptive algorithms for automatic control of train braking system

Assessment of adaptive algorithms for automatic control of t...

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International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)

作者： Andrejs Potapovs Anatoly Levchenkov Mikhail Gorobetz Rigas Tehniska universitate Riga LV

This paper provides a new insight into the smooth and precise adaptive railway transport braking system development. The system contains a controller with a control program based on an adaptive control algorithm and a current train braking control system ensures an automatic smooth and precise braking of a train and another controller ensures an automatic stopping of the train before the red light. Some of the adaptive search algorithms are studied and the task is to test and select the most suitable and the most effective of them. The computer model and simulation results are described in this paper.

关键词： Rail transportation Software algorithms Algorithm design and analysis adaptive algorithms adaptive systems Control systems Educational institutions

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Speech Quality Enhancement through Noise Cancellation using an adaptive Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 653页

作者： Kapoor, Janak Pathak, Ajita Rai, Manish Mishra, G.R. Dept. of Electronics and Communication Engineering Amity University Lucknow India Electronics and Communication Engineering Department Amity University Lucknow India Electronics and Communication Engineering Department FET MJP Rohilkhand University Bareilly India Physics and Electronics Department Dr. RML Avadh University Ayodhya India

Noise when added to the speech signal deteriorates its quality and makes the speech signal meaningless for the listeners. The active noise cancellation technique can be used for cancelling this noise, recovering the original signal, and making it meaningful. But due to fast varying characteristics of speech signals along with high sensitivity of speech for surrounding noise the use of active noise cancellation on speech signals is quite challenging. In this paper, this challenge of estimation and cancellation of speech signal noise is analysed. The proposed noise cancellation model is implemented using human's speech, birds chirp, and airplane sound as surrounding noise signals. The active noise cancellation is applied using least mean square algorithm (LMS), normalized least mean square algorithm (NLMS) and recursive least mean square algorithm (RLS) for different types of noisy surroundings. The prediction accuracy and SNR of all the three algorithms for real-time human speech are also compared and discussed. The results are supported by root mean square EVM power analysis. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： adaptive algorithms

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Multistage mixed precision iterative refinement

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NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS 2022年第4期29卷 e2434-e2434页

作者： Oktay, Eda Carson, Erin Charles Univ Prague Fac Math & Phys Prague Czech Republic

Low precision arithmetic, in particular half precision (16-bit) floating point arithmetic, is now available in commercial hardware. Using lower precision can offer significant savings in computation and communication costs with proportional savings in energy. Motivated by this, there has been a renewed interest in mixed precision iterative refinement schemes for solving linear systems Ax=b, and new variants of GMRES-based iterative refinement have been developed. Each particular variant with a given combination of precisions leads to different condition number-based constraints for convergence of the backward and forward errors, and each has different performance costs. The constraints for convergence given in the literature are, as an artifact of the analyses, often overly strict in practice, and thus could lead a user to select a more expensive variant when a less expensive one would have sufficed. In this work, we develop a multistage mixed precision iterative refinement solver which aims to combine existing mixed precision approaches to balance performance and accuracy and improve usability. For a user-specified initial combination of precisions, the algorithm begins with the least expensive approach and convergence is monitored via inexpensive computations with quantities produced during the iteration. If slow convergence or divergence is detected using particular stopping criteria, the algorithm switches to use a more expensive, but more reliable variant. A novel aspect of our approach is that, unlike existing implementations, our algorithm first attempts to use "stronger" GMRES-based solvers for the solution update before resorting to increasing the precision(s). In some scenarios, this can avoid the need to refactorize the matrix in higher precision. We perform extensive numerical experiments on a variety of random dense problems and problems from real applications which confirm the benefits of the multistage approach.

关键词： adaptive algorithms GMRES iterative refinement mixed precision

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Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem

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SOFT COMPUTING 2020年第10期24卷 7637-7684页

作者： Zhao, Ruxin Wang, Yongli Liu, Chang Hu, Peng Jelodar, Hamed Yuan, Chi Li, YanChao Masood, Isma Rabbani, Mahdi Li, Hao Li, Bo Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China CETC Big Data Res Inst Co Ltd Guiyang 550081 Guizhou Peoples R China Yangzhou Polytech Inst Sch Mech & Automot Engn Yangzhou 225127 Jiangsu Peoples R China

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples.

关键词： Selfish herd optimization algorithm Chaotic strategy IIR system identification problem adaptive algorithms Local optimization strategy

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An innovative fractional order LMS algorithm for power signal parameter estimation

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APPLIED MATHEMATICAL MODELLING 2020年 83卷 703-718页

作者： Chaudhary, Naveed Ishtiaq Latif, Rizwan Raja, Muhammad Asif Zahoor Tenreiro Machado, J. A. Int Islamic Univ Dept Elect Engn Islamabad Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Polytech Porto Dept Elect Engn Inst Engn Porto Portugal

Parameter estimation is an important issue for the quality monitoring and reliability assessment of power systems. In this study, an innovative fractional order least mean square (I-FOLMS) adaptive algorithm is presented for an effective parameter estimation. The I-FOLMS algorithm exploits the fractional gradient in its recursive parameter update mechanism, because its performance can be tuned by means of the fractional order. High values of the fractional order are good for fast convergence, but lead to steady state mis-adjustments. While, low values provide a smooth steady state behavior, but require a compromise in the convergence rate. The effective performance of I-FOLMS is verified and validated through two numerical examples of power signals estimation for different levels of noise variance and values of the fractional orders. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Parameter estimation Power signals Fractional calculus adaptive algorithms

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