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1st International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Nayak, Sasmita Choudhury, B. B. Lenka, Saroj Kumar Govt Coll Engn Dept Mech Engn Kalahandi Odisha India IGIT Dept Mech Engn Sarang Odisha India Mody Univ Dept Informat Technol Lakshmangarh India

ISBN: (纸本)9783319309330;9783319309323

Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).

关键词： Industrial robot selection gradient descent algorithm Backpropagation algorithm Neural network

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gradient descent based linear regression approach for modeling PID parameters

Gradient descent based linear regression approach for modeli...

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International Conference on Power Signals Control and Computations (EPSCICON)

作者： Parvathy, R. Devi, Rani Rajini Fed Inst Sci & Technol Dept Elect & Elect Engn Ernakulam 683577 Kerala India UKF Coll Engn & Technol Dept Mat Kollam 691302 Kerala India

ISBN: (纸本)9781479936120

The paper proposes a method for developing a linear model for Proportional Integral Derivative (PID) parameters of a PID controller. The approach is based on linear regression and it uses gradient descent algorithm for parameter optimization. A deep perusal of the literature shows that there have been many improvements made in developing PID tuning methods, and improving its performance. But a mention of a linear modeling of PID parameters is not noticed in any of the literature. The highlight of this method is the dependency of the tuning parameters on certain selected transient specifications. The speed control system of a dc motor is identified for the present study.

关键词： PID linear modeling regression gradient descent algorithm transient parameters

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Custom-IP for gradient descent Optimization based on Hardware/Software Co-design Paradigm 24

Custom-IP for Gradient Descent Optimization based on Hardwar...

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24th International Symposium on VLSI Design and Test (VDAT)

作者： Shabarinath, B. B. Muralidhar, P. Natl Inst Technol Warangal Telangana India

ISBN: (纸本)9781728193694

Field Programmable Gate Arrays (FPGA's) are being deployed in cloud data centers like Amazon Elastic compute cloud F1 instances for algorithmic acceleration to leverage the advantage of reconfigurability combined with high throughput and low power consumption of FPGA's and hence balancing the dynamic workloads. gradient descent is one such algorithm that is extensively used in core computation kernels to train the Machine Learning (ML) models. This paper proposes a custom-IP (Intellectual Property) for hardware acceleration of gradient descent algorithm (GDA) which is designed by exploring the inherent concurrency of GDA. The IP incorporates the Very High-Speed Integrated Circuit Hardware Description Language (VHDL) based description of GDA and AXI4 stream interface for connectivity. The Custom-IP is flexible and reusable by tuning generics defined in the VHDL code. The IP is interfaced to a 32-bit MicroBlaze soft-core processor which acts as host and manages run-time along with other peripherals to form a System on Chip (SoC) in which the design is partitioned into fixed hardware and flexible software. The Hardware/Software Co-design results show the 5x improvement in performance when implemented on Artix XC7A100T-CSG324 FPGA when compared to software implementation.

关键词： Amazon Elastic compute cloud F1 instance Field Programmable Gate Array gradient descent algorithm Hardware/Software Co-design System on Chip

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Localization in Wireless Sensor Networks with gradient descent

Localization in Wireless Sensor Networks with Gradient Desce...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Qiao, Dapeng Pang, Grantham K. H. Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781457702518

In this article, we present two distance-based sensor network localization algorithms. The location of the sensors is unknown initially and we can estimate the relative locations of sensors by using knowledge of inter-sensor distance measurements. Together with the knowledge of the absolute locations of three or more sensors, we can also determine the locations of all the sensors in the wireless network. The proposed algorithms make use of gradient descent to achieve excellent localization accuracy. The two gradient descent algorithms are iterative in nature and result is obtained when there is no further improvement on the accuracy. Simulation results have shown that the proposed algorithms have better performance than existing localization algorithms. A comparison of different methods is given in the paper.

关键词： Accuracy Adaptation models Distance measurement Equations Estimation Handheld computers Mathematical model distance measurement distance-based sensor network localization algorithm gradient descent algorithm gradient methods inter-sensor distance measurem

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Laboratory demonstration of wavefront based Stochastic Parallel gradient descent adaptive optics system

Laboratory demonstration of wavefront based Stochastic Paral...

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Conference on Atmospheric Optics - Models, Measurements, and Target-in-the-Loop Propagation

作者： Belen'kii, Mikhail S. Barchers, Jeff Berg, Eric Bruns, Don Fung, Deborah Gallan, Richard Kiro, Clay Runyeon, Hope Rye, Vincent Voass, Josh Trex Enterprises Corp 10455 Pacific Ctr Court San Diego CA 92121 USA

ISBN: (纸本)9780819468567

A laboratory demonstration of two novel tactical beam control methods for correcting the effects of strong turbulence including Beacon Anisoplanatism, and the combined effects of Beacon Anisoplanatism and Thermal Blooming, respectively, were performed in SAIC's Tactical Beam Control Test-Bed. Both systems were tested with ratio of aperture diameter to Fried parameter, D/r(0), of up to 7, and ratio of beam spot size at the target to isoplanatic angle, theta(B)/theta(O), of up to 10. The first method was implemented in a Wavefront-based Stochastic Parallel gradient Decent (WSPGD) adaptive optics (AO) system, which uses an off-axis wavefront sensor (WFS) to provide feedback for a multi-dithering beam control algorithm. The second method was implemented in a Hybrid WSPGD AO system, which incorporates the WSPGD AO system with a conventional Phase Conjugate (PC) AO, system. The Hybrid system uses an on-axis WFS to generate initial deformable mirror commands and an off-axis WFS to generate additional commands that account for the high frequency phase components removed from the wavefront of a laser return by Beacon Anisoplanatism. We developed a low speed PC-based WSPGD controller, implemented designs of the WSPGD and Hybrid WSPGD AO systems in SAIC's Test-Bed, and tested both AO systems in static and dynamic turbulence over a wide range of turbulence conditions. A target-plane tracker was used to stabilize the line-of-sight in the AO corrected beam. Test results show that the WSPGD AO system efficiently compensates the effects of Beacon Anisoplanatism for both static and dynamic turbulence, providing a mean performance gain of 1.8 averaged over multiple turbulent realizations. We also found in testing that the Hybrid WSPGD system efficiently compensates for Beacon Anisoplanatism in the presence of Thermal Blooming - providing improved compensation for both Thermal Blooming and turbulence. In the presence of strong Beacon Anisoplanatism with theta B/theta(O) of up to 10, t

关键词： turbulence Thermal Blooming adaptive optics wavefront sensor beam control gradient descent algorithm

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GMM-Guided gradient descent Learning of RBF Neural Network with Its Application on Robust GPS Satellites Selection

GMM-Guided Gradient Descent Learning of RBF Neural Network w...

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1st International eConference on Computer and Knowledge Engineering (ICCKE)

作者： Saraf, M. Mohammadi, K. Mosavi, M. R. Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran

ISBN: (纸本)9781467357135;9781467357128

In this paper, a novel gradient descent learning algorithm based on Gaussian Mixture Model (GMM) applied on Radial Basis Function Neural Network (RBFNN) is proposed in order to approximate the functions which have high non-linear order. How we can choose the strategy of gradient descent including learning coefficients selecting and really is it optimized to learn the same for all feature vectors?, are the challenges made us to think precisely on those. In this study, GMM estimates the probability density of the feature space and then the optimal learning rates can be evaluated proportional to these probabilities. These cause the neurons to learn correspondence with the feature distribution likelihoods. Considering robust satellite subset selection, Geometric Dilution of Precision (GDOP) factor is calculated for all subset of satellites and then the subset with lowest value is selected for positioning, but it is so non-linear and has computational burden to navigation systems. We use the proposed method to approximate it. The results on real GPS measurements demonstrate that it significantly track the non-linearity of GPS GDOP.

关键词： Gaussian Mixture Model (GMM) Geometric Dilution of Precision (GDOP) gradient descent algorithm Radial Basis Function Neural Network (RBFNN)

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Event-triggered adaptive gradient descent neural network backstepping control for a class of uncertain nonlinear systems 36

Event-triggered adaptive gradient descent neural network bac...

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36th Chinese Control and Decision Conference (CCDC)

作者： Yang, Jiaxing Yang, Xuebo Chen, Zhongbo Wang, Jiatong Zheng, Xiaolong Harbin Inst Technol Harbin Peoples R China Bohai Univ Jinzhou Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper presents an event-triggered adaptive neural network (NN) backstepping control method for a class of uncertain nonlinear systems. The combination of NN and backstepping technique solves the complexity explosion and compensates for the system unmodeled dynamics. At the same time, the NN weight trained by the gradient descent algorithm can ensure NN approximation performance to the unknown nonlinear function. Further, a smooth switching trigger strategy is introduced to achieve a lower updating frequency of the system control signal. According to the Lyapunov criterion, the tracking error of the system is semi-globally uniformly bounded and the system controller does not behave Zeno. Finally, the proposed method is applied to the simulation model of a DC motor control system.

关键词： neural network gradient descent algorithm event-triggered control smooth switching threshold

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Wavefront-based stochastic parallel gradient descent beam control

Wavefront-based stochastic parallel gradient descent beam co...

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Conference on Advanced Wavefront Control: Methods, Devices, and Applications V

作者： Belen'kii, Mikhail S. Hughes, Kevin Runyeon, Hope Rye, Vincent Trex Enterprises Corp San Diego CA 92121 USA

ISBN: (纸本)9780819468598

For a non-cooperative target, a laser beacon is created by illuminating the target with a beacon beam. When a beacon beam propagates though deep turbulence, turbulence spreads the beam. A conventional phase conjugate adaptive optics (AO) system is not efficient in the presence of Beacon Anisoplanatism when the beacon beam spot size at the target includes many isoplanatic patch sizes. We introduce a concept of the wavefront-based stochastic parallel gradient decent (WSPGD) AO system, which uses an off-axis wavefront sensor to provide feedback for the beam control algorithm. This concept is based on the finding that the phase aberrations of laser return from the target contain information about beam spot size at the target, and that correction of a limited number of low-order Zernike modes increases on-axis intensity and power in the bucket at the target. We evaluated the WSPGD AO system performance in simulation for two tactical engagement scenarios in the presence of strong turbulence. We found that that the WSPGD AO system can efficiently compensate the effects of strong turbulence including Beacon Anisoplanatism, even when the beam spot size at the target includes up to 20 isoplanatic patch sizes and the isoplanatic angle is by a factor of 2.6 less than the diffraction limit. The Strehl ratio gain for this scenario is 1.6 - 2.5, and the maximum Strehl ratio is achieved after 15-20 iterations. A laboratory demonstration performed under a separate program confirmed our theoretical predictions.

关键词： turbulence adaptive optics wavefront sensor beam control gradient descent algorithm

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gradient descent-Based Parameter Estimation for Ultra Short Baseline

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IEEE ACCESS 2024年 12卷 174713-174722页

作者： Kim, Yongcheol Ko, Haklim Lee, Hojun Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Hoseo Univ Dept Informat & Commun Engn Asan 31066 South Korea

This paper proposes a phase-based pseudorange estimation and a gradient descent-based localization algorithm to improve the positioning performance of the ultra short baseline (USBL) system. Traditional USBL systems estimate the pseudorange by cross-correlating the received chirp signal. Still, the limited sampling rate makes it difficult to estimate an accurate pseudorange, resulting in low positioning performance. The proposed method calculates the number of wavelengths of the received chirp signal and detects the initial phase of the signal to estimate a more precise pseudorange than existing methods. Additionally, the localization performance is enhanced using an optimization algorithm based on gradient descent. Through computer simulations and ocean experiments, the proposed method demonstrated up to 29 times better localization performance in computer simulations and up to 107 times better performance in ocean experiments compared to existing methods, verifying that the proposed method outperforms the existing methods.

关键词： gradient descent algorithm pseudorange source localization source localization ultra short baseline (USBL) ultra short baseline (USBL) underwater sensor networks underwater sensor networks underwater sensor networks

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Improved gradient descent bit flipping decoder for LDPC codes on BSC channel

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IEICE ELECTRONICS EXPRESS 2018年第8期15卷 20180195-20180195页

作者： Ren, Dao Sha, Jin Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Jiangsu Peoples R China Nanjing Univ Shenzhen Res Inst Nanjing 210046 Jiangsu Peoples R China

Low-Density Parity-Check (LDPC) codes have capacity-approaching error-correction performance which makes them popular in communication system. gradient descent bit flipping (GDBF) algorithms show an error correction capability superior to most known BF algorithms. In this paper, we propose an improved gradient descent bit flipping (IGDBF) algorithm for LDPC codes on BSC channel. Compared to GDBF algorithm, the proposed algorithm reconstructs the composition of energy function, and adds a penalty term to help it converge. Simulations show that the proposed algorithm has good performance and fast convergence rate.

关键词： LDPC code bit flipping algorithm gradient descent algorithm flipping times

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