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检索条件"主题词=Gradient Descent Algorithm"

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SHA-Less Pipelined ADC Converting 10th Nyquist Band with In-Situ Clock-Skew Calibration

SHA-Less Pipelined ADC Converting 10th Nyquist Band with In-...

引用

32nd Annual Custom Integrated Circuits Conference CICC

作者： Huang, Pingli Hsien, Szukang Lu, Victor Wan, Peiyuan Lee, Seung-Chul Liu, Wenbo Chen, Bo-Wei Lee, Yung-Pin Chen, Wen-Tsao Yang, Tzu-Yi Ma, Gin-Kou Chiu, Yun Univ Illinois Urbana IL 61801 USA Texas Instruments Inc Sunnyvale CA USA Beijing Univ Technol Beijing Peoples R China Ind Technol Res Inst Hsinchu Taiwan

ISBN: (纸本)9781424457601

Conversion from dc to the 10th Nyquist band is enabled in a SHA-less, 10-b, 100-MS/s pipelined ADC by digitally calibrating the clock skew in the 3.5-b front-end stage. Architectural redundancy of pipelined ADC is exploited to extract skew information from the first-stage residue output with two out-of-range comparators and some simple digital logic;a gradient-descent algorithm is used to adaptively adjust the timing of the front-end sub-ADC to synchronize with that of the S/H. The 90-nm prototype consumes 12.2 mW and digitizes inputs up to 480 MHz (limited by testing equipment) without skew errors in experiments, whereas the same ADC fails at 130 MHz when the calibration is disabled. The measured SFDR is 71 dB at 20 MHz and 55 dB at 480 MHz.

关键词： Nyquist band SHA-less pipelined ADC adaptive timing adjustment analogue-digital conversion calibration clock skew clocks digital logic gradient descent algorithm gradient methods in-situ clock-skew calibration out-of-range comparator pipeline processing power 12.2 mW size 90 nm timing

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A Method of Dynamic System Identification based on Memory RBF Network 28

A Method of Dynamic System Identification based on Memory RB...

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28th Chinese Control and Decision Conference

作者： Lv, Qiang Zhang, Yang Lin, Huican Acad Armored Force Engn Beijing 100072 Peoples R China

ISBN: (纸本)9781467397148

A recursive network is proposed by introducing memory neurons based on the RBF network. Due to the current output value of the network is related to the past input value in the network, the network will be able to identify the dynamics of the system without the need of explicitly feedback of input and output in the past. Thus, the network is able to identify a system has an unknown order or an unknown delay. Training algorithm and the theoretical rationality are provided in this paper. The validity of the method is verified by simulation of dynamic system identification, and it is of great significance in the field of adaptive control.

关键词： RBF Network System Identification Recursive Network gradient descent algorithm

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Suboptimal Adaptive Receding Horizon Control Using Simplified Nonlinear Programming 25

Suboptimal Adaptive Receding Horizon Control Using Simplifie...

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25th IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Issa, Hazem Khan, Hamza Tar, Jozsef K. Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary Natl Text Univ Dept Text & Clothing Karachi Campus Karachi 74900 Pakistan

ISBN: (纸本)9781665444996

In this paper, an "Adaptive Receding Horizon Controller (ARHC)" is exemplified in the suboptimal control of a Furuta pendulum. A dynamic model of strongly overestimated inertia and friction parameters is used in an RHC controller to track the nominal trajectory under cost terms penalizing the control forces. The so obtained "optimized" trajectory is tracked by an adaptive controller that uses a realistic approximate dynamic model of the controlled system. Since the approximate and the actual model contain considerably smaller inertia and friction parameters than that used for optimization the cautiously optimized trajectory can be precisely tracked by the actual system without suffering from heavy force burdens. The adaptivity is guaranteed by a "Fixed Point Iteration"-based approach that in this manner easily can be combined with the mathematical framework of optimal controllers. Instead of using Lagrange multipliers, the optimization happens through explicitly applying the dynamic model in forward Euler integration. The operation of the method is exemplified via numerical simulations.

关键词： Optimal Control Model Predictive Control Receding Horizon Control Nonlinear Programming Reduced gradient algorithm gradient descent algorithm

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A New Computational Method for Solving Fully Fuzzy Nonlinear Systems 10th

A New Computational Method for Solving Fully Fuzzy Nonlinear...

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10th International Conference on Computational Collective Intelligence (ICCCI)

作者： Jafari, Raheleh Razvarz, Sina Gegov, Alexander Univ Agder CAIR Grimstad Norway Natl Polytech Inst Dept Control Automat CINVESTAV IPN Mexico City DF Mexico Univ Portsmouth Sch Comp Buckingham Bldg Portsmouth PO13HE Hants England

ISBN: (纸本)9783319984438;9783319984421

Predicting the solution of complex systems is a significant challenge. Complexity is caused mainly by uncertainty and nonlinearity. The nonlinear nature of many complex systems leaves uncertainty irreducible in many cases. In this work, a novel iterative strategy based on the feedback neural network is recommended to obtain the approximated solutions of the fully fuzzy nonlinear system (FFNS). In order to obtain the estimated solutions, a gradient descent algorithm is suggested for training the feedback neural network. An example is laid down in order to demonstrate the high accuracy of this suggested technique.

关键词： Approximate solution Complex system Feedback neural network gradient descent algorithm Simulation

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Learning to Rank Relational Objects Based on the Listwise Approach

Learning to Rank Relational Objects Based on the Listwise Ap...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ding, Yuxin Zhou, Di Xiao, Min Dong, Li Harin Inst Technol Shenzhen Grad Sch Dept Comp Sci & Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9781424496365

In recent years machine learning technologies have been applied to ranking, and a new research branch named "learning to rank" has emerged. Three types of learning-to-rank methods - pointwise, pairwise and listwise approaches - have been proposed. This paper is concerned with listwise approach. Currently structural support vector machine(SVM) and linear neural network have been utilized in listwise approach, but these methods only consider the content relevance of an object with respect to queries, they all ignore the relationships between objects. In this paper we study how to use relationships between objects to improve the performance of a ranking model. A novel ranking function is proposed, which combines the content relevance of documents with respect to queries and relation information between documents. Two types of loss functions are constructed as the targets for optimization. Then we utilize neural network and gradient descent algorithm as model and training algorithm to build ranking model. In the experiments, we compare the proposed methods with two conventional listwise approaches. Experimental results on OHSUMED dataset show that the proposed methods outperform the conventional methods.

关键词： Entropy Feature extraction Machine learning Stochastic processes Training Vectors Web pages content relevance gradient descent algorithm gradient methods learning (artificial intelligence) learning-to-rank method linear neural network listwise approach loss functions machine learning neural nets optimisation optimization pairwise approach pointwise approach ranking function ranking model relational objects structural support vector machine support vector machines

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Modified gradient Linear Discriminant Analysis for Face Recognition

Modified Gradient Linear Discriminant Analysis for Face Reco...

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International Symposium on Knowledge Acquisition and Modeling

作者： He, Yunhui Nanjing Univ Informat Sci & Technol Dept Commun Engn Nanjing Peoples R China

ISBN: (纸本)9781424435296

In this paper, we propose two kinds of orthogonalization gradient linear discriminant analysis (OGLDA) algorithms to improve the performance of traditional gradient LDA (GLDA) for undersampled problems in face recognition tasks. In the OGLDA1, the orthogonalization procedure is performed on the discriminant vectors to reduce the redundancy among the discriminant features obtained by GLDA. Thus, all obtained discriminative features can equally contribute to classification performance, which significantly improves the performance of GLDA algorithm for face recognition. In the OGLDA2, the discriminant vectors are resolved one by one in each iterative procedure which overcomes the drawbacks of high computational cost and numerical instability existing in the OGLDA1 algorithm. Since the orthogonalization procedure is applied in the proposed OGLDA methods, the computational stability is improved greatly. The effectiveness of the proposed methods is verified in the experiments on the standard face image databases.

关键词： face recognition linear discriminant analysis orthogonalization gradient descent algorithm

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An adaptive fuzzy PID control for a class of SISO uncertain nonlinear systems 5

An adaptive fuzzy PID control for a class of SISO uncertain ...

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5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Cherrat, Nidhal Boubertakh, Hamid Arioui, Hichem Univ Jijel LAJ Jijel Algeria Univ Evry Val Essonne Lab IBISC EA 4526 Paris France

ISBN: (纸本)9781538606865

In this work we propose an adaptive PID control law to deal with a class of single input single output (SISO) uncertain nonlinear systems. In fact, a fuzzy system is used for approximating the PID control gains. The fuzzy system parameters are adjusted online using a robust adaptation law based on the gradient method and augmented by the so-called "e-modification" term, in order to minimize the error between the fuzzy PID controller and the unknown ideal controller. The stability of the closed-loop system is proven analytically using the Lyapunov approach. A simulation example is presented to illustrate the performance of the proposed scheme.

关键词： PID control Uncertain nonlinear systems Fuzzy systems gradient descent algorithm Lyapunov stability

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Optimal Control of Integrated Losses in Dual Active Bridge DC-DC Converters Under Extended Modulation

Optimal Control of Integrated Losses in Dual Active Bridge D...

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International Conference on Wireless Power Transfer (ICWPT)

作者： Hao, Rui Wang, Xiaolan Wei, Tengfei Wang, Jiarui Wang, Lixin Yue, Meng Li, Zhaohui Lanzhou Univ Technol Univ Lanzhou 730000 Peoples R China

ISBN: (纸本)9789819708673;9789819708659;9789819708642

In order to reduce the overall power loss of the bidirectional active bridge (DAB) converter and optimise its dynamic characteristics;this paper proposes a hybrid optimal control strategy based on the output voltage model predictive control of extended phase-shift modulation and gradient descent algorithm. The state space model of the DAB converter is established by analysing its operating modes under extended phase shift;the switching tube and transformer losses of the DAB converter are investigated to obtain the integrated loss model of the DAB converter;and the output voltage model predictive control is combined with the gradient descent algorithm to optimise the target power loss. Simulation results show that compared with the traditional PI control, the transfer efficiency of the method proposed in this paper is improved by about 3.7%;the dynamic recovery time is reduced by about 80% and the output voltage fluctuation is reduced by about 76% when the input voltage and the load are perturbed, which verifies the effectiveness of the control strategy.

关键词： Extended Phase Shift Modulation Integrated loss optimization gradient descent algorithm

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Wavelet Neural Network based Controller Performance Analysis of a PMBLDC Motor

Wavelet Neural Network based Controller Performance Analysis...

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International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Ghosh, Bashudeb Chandra Hossen, Md. Belal Khulna Univ Engn & Technol EEE Dept Khulna Bangladesh

ISBN: (纸本)9781538682524

Wavelet Neural Networks (WNN) are computational tools used now a day for control and estimation purposes. This paper describes a wavelet neural network based control system designed for Permanent Magnet Brushless DC (PMBLDC) Motor drive. The WNN controller is trained online through gradient descent algorithm for controlling the motor output in different operating conditions. A software is developed in C++ and was used to test the performance of the drive system. The results show that drive control system works effectively after comparison of the results with those obtained using PI controller based system.

关键词： PMBLDC motor WNN online training gradient descent algorithm FOC and Trapezoidal back emf

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UNMIXING HYPERSPECTRAL IMAGES USING THE GENERALIZED BILINEAR MODEL

UNMIXING HYPERSPECTRAL IMAGES USING THE GENERALIZED BILINEAR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Halimi, Abderrahim Altmann, Yoann Dobigeon, Nicolas Tourneret, Jean-Yves Univ Toulouse IRIT INP ENSEEIHT TeSA Toulouse France

ISBN: (纸本)9781457710056

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

关键词： hyperspectral imagery spectral unmixing bilinear model Bayesian inference MCMC methods gradient descent algorithm least square algorithm

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