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Adaptive Fuzzy PID Control for a Class of Uncertain MIMO Nonlinear Systems with Dead-zone Inputs' Nonlinearities

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2018年第1期42卷 21-39页

作者： Cherrat, Nidhal Boubertakh, Hamid Arioui, Hichem Univ MSBY Jijel LAJ Jijel Algeria Univ Evry Val Essonne IBISC Lab EA2456 Evry France

This paper proposes an adaptive fuzzy PID control for a class of multi-input multi-output disturbed nonlinear systems with unknown dynamics and unknown dead-zone inputs' nonlinearities. Within this scheme, a fuzzy system is used in order to approximate the PID gains instantaneously according to whole control system objective. Specifically, a gradient descent algorithm is used to update online the fuzzy system parameters such that the error between the fuzzy PID control and the unknown ideal control converges to an adjustable region. The control design and the stability analysis of the closed-loop system are given by the Lyapunov approach. The effectiveness of the proposed controller is highlighted by simulated examples.

关键词： PID control Fuzzy systems Adaptive fuzzy control MIMO nonlinear systems gradient descent algorithm Lyapunov stability

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ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 2305-2314页

作者： Pattanaik, Priyadarshini A. Khan, Mohammad Zubair Patnaik, Prasant Kumar IMT Atlantique Bretagne Pays Loire Plouzane Dept Image & Traitement Informat F-29238 Brest France Taibah Univ Dept Comp Sci Madinah Saudi Arabia KIIT Univ Sch Comp Engn Bhubaneswar 751024 India

Deep Convolutional Neural Networks (CNNs) are the heart of deep neural network research and have accomplished remarkable masterstrokes in various domains. In this research, a new attention-driven deep neural network approach is proposed to localize and classify late blight crop disease by improving the embedding network performance using visual attention. Early disease detection is critical to plan expeditiously and reduce crop losses. Late blight diagnosis is still mainly performed physically. Additionally, this work also expands the visualized gradient-based method by tiding over the gap and facilitates the exploration of the rich dynamics of the core behavioral trained CNNs to identify the unusual patterns that are hidden behind huge data to localize the target object. Studies on decision support for target localization have increased drastically achieving very significant results using deep learning techniques that still fail to explain the black box. As proof of concept, we applied our approach assessing the ongoing benchmark expertly curated images on healthy and late blight contaminated harvests through the current online stage PlantVillage. Experimental comparative results show that our proposed approach achieves a test accuracy of 98.99%, which offers higher localization with classification and assists with taking care of the issue of yield misfortunes in harvests because of irresistible infectious diseases. The result of this research will upgrade the implementation of a deep neural network for early disease diagnosis and management in the agricultural field.

关键词： Crop diseases Late blight Convolutional neural network gradient descent algorithm Resnet Deep learning Infectious disease Localization Classification Plant pathology

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Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

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JOURNAL OF NEUROPHYSIOLOGY 2022年第2期127卷 529-547页

作者： Scano, Alessandro Mira, Robert Mihai d'Avella, Andrea Italian Natl Res Council CNR UOS STIIMA Lecco Human Ctr Smart & Safe Living En Lecce Italy IRCCS Fdn Santa Lucia Lab Neuromotor Physiol Rome Italy Univ Messina Dept Biomed & Dent Sci & Morphofunct Imaging Messina Italy

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule that overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and electromyographic data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data;MMF can also be applied to any multivariate data that contain both non- negative and unconstrained variables. NEW & NOTEWORTHY The mixed matrix factorization (MMF) is a novel method for extracting kinematic-muscular synergies. The previous state of the art algorithm (NMFpn) factor

关键词： EMG gradient descent algorithm kinematic-muscular synergies kinematics synergy extraction

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Estimation and analysis of vegetation parameters for the water cloud model

River

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River 2024年第4期3卷 399-407页

作者： Xiangdong Qin Zhiguo Pang Jingxuan Lu Department of Drought Mitigation and Policy Research Research Center on Flood and Drought Disaster Reduction of the Ministry of Water ResourcesChina Institute of Hydraulic and Hydropower ResearchBeijingChina State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijingChina

The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion *** parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of empirical parameters only considers the forward simulation process,and insufficient consideration is given to the model inversion *** study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective *** effectiveness of the method is then verified using measured ***,this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM,resulting in the following ***,blindly utilizing vegetation parameters recommended by previous model studies is not *** ensure the accuracy of the simulation,it is necessary to adjust the vegetation parameters ***,to ensure the ability of the WCM solving both forward and inverse problems,it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost ***,soil backscatter simulations have an impact on the solution of vegetation parameters,and more accurate soil scattering models provide a better representation of the modeled *** study presents a dependable method for resolving the vegetation parameters of the WCM,thereby offering a valuable reference for the application of the model in surface parameter inversion research.

关键词： backscattering coefficient gradient descent algorithm objective function soil moisture water cloud model

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Quality of service (QoS) parameters prediction for web services using hybrid neural network and ensemble methods

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2019年第6期10卷 1577-1614页

作者： Kumar, Lov Sureka, Ashish BITS Pilani Dept Comp Sci & Engn Pilani Rajasthan India Ashoka Univ Sonipat India

Web services are an essential part of the everyday operation for a number of organizations. Applications within organizations frequently depend on web services to fulfill customers needs. Failed and slow online application depending on web services can cost a lot to organizations in terms of customer dissatisfaction. Estimation and improvement of the quality of service (QoS) parameters of web service serve as the primary objective of service providers. We hypothesize that analyzing the source code behind the web services helps in computing the QoS parameters. In this paper, we analyze the source code using thirty-seven different source code metrics (SCMs) to measure the internal structure of the software system. In order to develop a model, these SCMs are treated as input for predicting QoS parameters using nine different prediction techniques and three different ensemble methods. Since SCM act as an important parameter for assessing the performance of prediction techniques, hence for dimensionality reduction and removing irrelevant features, four distinct feature reduction techniques are considered in the study during implementation. Two distinct evaluation criteria such as MMRE and RMSE, are considered for evaluating and comparing the performance of the developed QoS parameters prediction models. The experimental results reveal that the weighted-based ensemble method produces better results. The results also depict that the ME, Ca, IC, DAM, and MFA metrics are commonly found relevant metrics for QoS parameters prediction. Furthermore, we also observe that the selected set of metrics achieves better results compared to all metrics.

关键词： Web services Service oriented computing Ensemble methods Feature selection technique Neural network gradient descent algorithm Genetic algorithm Quality of service (QoS) parameters

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Joint variable and variable projection algorithms for separable nonlinear models using Aitken acceleration technique

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2023年第3期42卷 202-210页

作者： Cheng, Lianyuan Chen, Jing Rong, Yingjiao Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi Peoples R China

This paper proposes a joint variable-based gradient descent algorithm (Joint-GD) and a variable projection (VP)-based gradient descent algorithm (VP-GD) for separable nonlinear models. The VP algorithm takes advantage of the separability property of variables to reduce the dimensionality of the parameters, which makes the convergence rates faster. In order to speed up the convergence of the gradient descent algorithm, the Aitken acceleration technique is introduced in the algorithms, which is second-order convergent. Moreover, the Aitken-based methods are robust to the step-size, therefore they can be widely used in engineering practices. The numerical simulation shows the effectiveness of the proposed algorithms.

关键词： variable projection algorithm joint variable algorithm gradient descent algorithm separable nonlinear model Aitken acceleration technique

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TARGET THREAT ASSESSMENT BASED ON MODIFIED PARTICLE SWARM OPTIMIZED FUZZY RECURRENT WAVELET NEURAL NETWORK

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2017年第2期13卷 605-620页

作者： Chen, Xia Shi, Kun Li, Xin Shenyang Aerosp Univ Sch Automat 37 Daoyi South Ave Shenyang 110136 Liaoning Peoples R China Nanjing Univ Aeronaut & Astronaut Inst Bioinspired Struct & Surface Engn Coll Mech & Elect Engn 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

In this research paper, a practical novel hybrid model to assess the value of target threat degree was proposed. The model was based on modified particle swarm optimization (MPSO) in combination with fuzzy recurrent wavelet neural network (FRWNN), namely MPSO optimized fuzzy recurrent wavelet neural network (MPSO-FRWNN). Moreover, a single neuron employed in consequent part of each fuzzy rule of FRWNN is capable of storing the previous data of the networks instead of conventional Takagi-Sugeno-Kang (TSK) fuzzy model. This optimization mechanism involved a hybrid training procedure integrating MPSO and gradient descent algorithm (GDA), which significantly enhances the prediction or assessment accuracy. To locate a reasonably good region in the continuous search space, a new adjustment scheme named MPSO algorithm is developed, which includes two inline-PSO processes, and thus it can fit well with the consequent forecasting learning based gradient descent optimization. Finally, conclusions of this study are exposed by three comparative threat assessment experiments.

关键词： Particle swarm optimization Takagi-Sugeno-Kang fuzzy model gradient descent algorithm Threat assessment

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Optimization of Regression Analysis by Conducting Parallel Calculations 5

Optimization of Regression Analysis by Conducting Parallel C...

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5th International Conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Mochurad, Lesia Lviv Polytech Natl Univ Artificial Intelligence Dept UA-79013 Lvov Ukraine

The regression analysis of large data sets by parallelization is investigated in the work. Different approaches to parallel computing are proposed. The parallelization of the gradient descent method and the stochastic gradient descent using OpenMP and MPI technologies, as well as their hybrid, is performed. The efficiency of the proposed parallel algorithms is analyzed. A number of numerical experiments were performed. Acceleration of 5 times was achieved using the six-core architecture of a personal computer. By varying the number of threads and processor cores, the possibility of further optimization of the computational process is established. The problem of erroneous exchange is considered, which often has a negative effect on acceleration during parallelization using OpenMP. The advantages and disadvantages of using each of the technologies are analyzed. Without reducing the generality, the software product is developed in the work, which can be used in the regression analysis of big data processing in a wide range of economic problems.

关键词： gradient descent algorithm multicore OpenMP technology MPI technology acceleration

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A Contactless Insulator Contamination Levels Detecting Method Based on Infrared Images Features and RBFNN

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2019年第3期55卷 2455-2463页

作者： He, Hongying Luo, Diansheng Lee, Wei-Jen Zhang, Zhenyuan Cao, Yijia Lu, Tianguang Hunan Univ Changsha 410082 Hunan Peoples R China Univ Texas Arlington Arlington TX 76019 USA Univ Elect Sci & Technol China Chengdu 610054 Peoples R China Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

A contactless method uses infrared image features and radial basis function neural network (RBFNN) to detect contamination levels for porcelain insulators is proposed in this paper. First, theory evidence for contamination levels detection by infrared images is inferred. Then, the denoising and image segmentation is implemented to suppress the image noise and eliminate the affection of the background. Nine color moment features related to the contamination levels are extracted from the insulator images. Finally, an RBFNN is constructed to identify the contamination levels. The images features and ambient relative humidity are taken as the inputs of the RBFNN. For improve the precision of the detection, a new method based on the statistical probability of the values of each contamination feature component is proposed to select the initial hidden center parameters for RBFNN hidden nodes. An improved learning algorithm combined with the gradient descent algorithm and a random number control factor are proposed to modify the hidden center parameters and the weights vectors. Testing results show that the selected color moment features are effective on the contamination levels representation and the constructed RBFNN performs better than back propagation neural network (BPNN) and generalized regression neural network (GRNN) on contamination levels identification.

关键词： Color moment features contamination levels identification gradient descent algorithm hidden center parameters image processing infrared image insulators radial basis function neural network (RBFNN) random number control factor

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Extended content-boosted matrix factorization algorithm for recommender systems 18th

Extended content-boosted matrix factorization algorithm for ...

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18th Annual International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Krasnoshchok, Oleksandr Lamo, Yngve Bergen Univ Coll N-5020 Bergen Norway

Recommender technologies have been developed to give helpful predictions for decision making under uncertainty. An extensive amount of research has been done to increase the quality of such predictions, currently the methods based on matrix factorization are recognized as one of the most efficient. The focus of this paper is to extend a matrix factorization algorithm with content awareness to increase prediction accuracy. A recommender system prototype based on the resulting Extended Content-Boosted Matrix Factorization algorithm is designed, developed and evaluated. The algorithm has been evaluated by empirical evaluation, which starts with creating of an experimental design, then conducting off-line empirical tests with accuracy measurement. The result revealed further potential of the content awareness in matrix factorization methods, which has not been fully realized in the generalized alignment-biased algorithm by Nguyen and Zhu and uncovers opportunities for future research. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： gradient descent algorithm Content Aware Collaborative Filtering algorithm

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