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检索条件"主题词=Gradient descent algorithm"

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gradient based Optimal State Feedback Control design for Feedback Linearizable Nonlinear Nonaffine system 3

Gradient based Optimal State Feedback Control design for Fee...

引用

3rd IEEE International Conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Raj, Kavyasree S. Shihabudheen, K., V Natl Inst Technol Dept Elect Engn Calicut Kerala India

ISBN: (纸本)9781665425360

In this paper, an optimal state feedback controller is designed for a nonaffine nonlinear system. The nonaffine system is converted into affine system using mean value theorem, since the analysis of affine nonlinear system is easy compared to nonaffine systems. Feedback linearization is used for linearizing the affine system. Then a gradient descent algorithm-based optimization approach is designed for feedback linearized system for optimal performance. The performance of the proposed method is presented by conducting simulation study on magnetic levitation system. A significant reduction in the minimum value of cost function is obtained using proposed method compared with the conventional LQR technique and PSO method.

关键词： Feedback Linearization Nonaffine nonlinear system Mean value theorem gradient descent algorithm Hessian Matrix Particle Swarm Optimization (PSO)

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Sparse Kernel Regression for Traffic Flow Forecasting

Sparse Kernel Regression for Traffic Flow Forecasting

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8th International Symposium on Neural Networks

作者： Huang, Rongqing Sun, Shiliang Liu, Yan E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China

ISBN: (纸本)9783642210891

In this paper, a new kernel regression algorithm with sparse distance metric is proposed and applied to the traffic flow forecasting. The sparse kernel regression model is established by enforcing a mixed (2,1)-norm regularization over the metric matrix. It learns a mahalanobis metric by a gradient descent procedure, which can simultaneously remove noise in data and lead to a low-rank metric matrix. The new model is applied to forecast short-term traffic flows to verify its effectiveness. Experiments on real data of urban vehicular traffic flows are performed. Comparisons with two related kernel regression algorithms under three criterions show that the proposed algorithm is more effective for short-term traffic flow forecasting.

关键词： Traffic flow forecasting Kernel regression Sparse distance metric learning Mixed norm regularization gradient descent algorithm

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A PSO-GD-BASED HYBRID algorithm FOR GENERAL FUZZY MEASURE DETERMINATION

A PSO-GD-BASED HYBRID ALGORITHM FOR GENERAL FUZZY MEASURE DE...

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International Conference on Machine Learning and Cybernetics

作者： Zhao, Huan-Yu Wang, Xi-Zhao Hebei Univ Coll Math & Comp Sci Key Lab Machine Learning & Computat Intelligence Baoding 071002 Hebei Peoples R China

ISBN: (纸本)9781424447053

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

关键词： Fuzzy measure Fuzzy integral Particle swarm optimization gradient descent algorithm

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Efficient active control of fluid borne pulsation in hydraulic piping systems

Efficient active control of fluid borne pulsation in hydraul...

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IEEE/CSAA International Conference on Aircraft Utility Systems (AUS)

作者： Zheng, Huiwei Jiao, Zongxia Xu, Yuanzhi Chai, Lingyun Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781509010875

With the development of the hydraulic system of higher pressure and power, the fluid borne pulsation in pipelines becomes extremely harmful to the whole system. Therefore, the control of fluid borne pulsation is very significant for improving the performance of practical hydraulic systems. The traditional active control methods of fluid borne pulsation were found to be effective, while the existing weakness is model-based with huge computational consumption and poor robustness. A novel active control method is proposed here, using efficient gradient descent algorithm for parametric optimization. A fast servo valve is utilized as the actuator in the bypass fluid line. Both numerical and experimental verification are carried out. More than 70% attenuation of pressure peaks can be obtained in the simulation, and experiments on a practical hydraulic system achieve about 35% pulsation attenuation. The performance on computation efficiency is also good compared with conventional methods, which means the present control method is effective and efficient.

关键词： Fluid borne pulsation active control method gradient descent algorithm piping system

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A CONTROL-ORIENTED HYBRID MODEL FOR NUCLEAR REACTORS BASED ON NEURAL NETWORK 31

A CONTROL-ORIENTED HYBRID MODEL FOR NUCLEAR REACTORS BASED O...

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31st International Conference on Nuclear Engineering (ICONE) - 24/7 Clean Nuclear Energy - Solution for Global Warming

作者： Zhu, Ze Liang, Wenlong Ye, Baiqing Jiang, Qingfeng Wang, Pengfei Xi An Jiao Tong Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9780791888216

Accurate modeling is the basis for analyzing the dynamic response characteristics of the model. However, due to the complexity and time-varying nature of the internal mechanisms of the reactor, it is inevitable that inaccurate model parameters will be used in the modeling process. These will lead to discrepancies between the modeled mechanisms and the actual reactor. In this paper, the difference is evaluated and shortened by means of neural network hybrid modeling. Based on the MATLAB/Simulink simulation platform, this paper firstly obtains the parameters that have the greatest influence on the linear model through sensitivity analysis and takes them as the object of neural network correction, then obtains the data required for offline training of neural network according to the mechanism model, linear model and the deviation of the two under different working condition levels, retains the neural network weights and thresholds obtained from the offline training, and finally utilizes the gradient descent algorithm to update the neural network weights and thresholds in real time in order to achieve the online calibration of the linear model. The final results show that the hybrid model can effectively reduce the steady-state deviation between the two models, which indicates that the hybrid modeling can effectively improve the accuracy of the established model and provide a solid foundation for the subsequent design of the control system based on the linear model.

关键词： Hybrid modeling Mechanism model Linear model Neural network gradient descent algorithm

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ANN Approach for Magnetic Levitation Stabilization Using gradient and Quasi Newton Learning

ANN Approach for Magnetic Levitation Stabilization Using Gra...

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Students Conference on Engineering and Systems (SCES)

作者： Saini, Arun Kumar Sharma, Veena NIT Dept Elect Engn Hamirpur 177005 HP India

ISBN: (纸本)9781479949397

Magnetic Levitation (Maglev) stabilization has been the area of interest for various engineering fields. Classical controllers (like PID) can handle linear plants easily but when the plants are non-linear they have difficulties to deal with. This paper presents the Neural Network controller that are nonlinear in nature and can handle the controlling of nonlinear plants. The gradient descent algorithm is used to minimize the error function. Further the results of NN (Neural Network) controller are improved using Conjugate gradient learning and Quasi Newton methods. The results are presented to show better tracking behavior after applying different learning algorithms.

关键词： ANN (Artificial Neural Network) BGFS (Broyden Fletcher Goldfarb Shanno) algorithm Fletcher Reeves algorithm gradient descent algorithm Levenberg Marquardt algorithm

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Unequally-Spaced PAM-4 Enabled Power Budget Enhancement of UDWDM-PON Utilizing Simplified Coherent Receiver

Unequally-Spaced PAM-4 Enabled Power Budget Enhancement of U...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhou, Jiajun Chen, Junda Wang, Hongli Fu, Songnian Tang, Ming Hubei Jiufengshan Laboratory Wuhan China National Engineering Laboratory for Next Generation Internet Access System Wuhan China Guangdong University of Technology Engineering Guangdong University of Technology Institute of Advanced Photonics Technology School of Information Engineering Guangzhou China

ISBN: (纸本)9798350312614

We numerically investigate a 4×10 Gb/s cost-effective coherent ultra-dense wavelength division multiplexing passive optical network (UDWDM-PON) by the use of unequally-spaced 4-level pulse-amplitude modulation (UES-PAM-4) signal based on gradient descent algorithm. According to the simulation results, a receiver sensitivity of -35.2 dBm for a single wavelength at the bit-error ratio (BER) of 3.8×10-3 is obtained. Compared with the conventional equally-spaced PAM-4 (ES-PAM-4) signal, a 2.5 dB receiver sensitivity enhancement can be secured after 20-km standard single-mode fiber (SSMF) transmission. Meanwhile, the UES-PAM-4 signal is numerically verified for 4×10 Gb/s UDWDM-PON. An average receiver sensitivity of -37.5 dBm and a power budget of 38.5 dB are obtained after the 20-km SSMF transmission. The proposed UES- PAM -4 scheme with the receiver sensitivity enhancement is a promising candidate for the UDWDM-PON in the existing optical distribution network (ODN). © 2023 IEEE.

关键词： gradient descent algorithm PAM-4 passive optical network power budget simplified coherent receiver

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Maximum Likelihood Detection of Nonlinearly Distorted OFDM Signal 58

Maximum Likelihood Detection of Nonlinearly Distorted OFDM S...

引用

IEEE Global Telecommunications Conference (GLOBECOM)

作者： Regev, Nir Iofedov, Ilia Wulich, Dov Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9781479959525

This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrier decision, used when the linear PA is considered, is no longer optimal. We propose a sub-optimal receiver based on the Maximum Likelihood (ML) criterion. The ML receiver has to find the minimum Euclidean distance between the received vector and a set of all possible OFDM symbols passed through the same nonlinearity. This approach has exponential complexity. To reduce the complexity, we propose a sub-optimal receiver that minimizes the Euclidean distance, seen as a cost function, by the gradient descent algorithm. Unfortunately, due to the nonlinearity, the cost function is non-convex. In order to overcome this obstacle, we propose a method to classify the solution, i.e., to decide if the achieved minimum is local or global. We modify the gradient descent algorithm to avoid convergence to a local minimum. It is shown that the proposed receiver outperforms the simple OFDM and iterative receivers in terms of symbol error rate (SER) performance.

关键词： OFDM Nonlinear Power Amplifier Maximum Likelihood gradient descent algorithm

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Z-Adaptive Fuzzy Inference Systems

<i>Z</i>-Adaptive Fuzzy Inference Systems

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IEEE CIS International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Rezaee-Ahmadi, Fatemeh Rafiei-R, Hamed Akbarzadeh-T, Mohammad-R Ferdowsi Univ Mashhad Dept Elect Engn Ctr Excellence Soft Comp & Intelligent Informat P Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781665444071

Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.

关键词： Z-numbers Fuzzy inference systems Uncertainty Reliability gradient descent algorithm

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Numerical Solution of a Class of Singular Boundary Value Problems Arising in Physiology Based on Neural Networks 5th

Numerical Solution of a Class of Singular Boundary Value Pro...

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5th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Yadav, Neha Kim, Joong Hoon Yadav, Anupam Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Natl Inst Technol Dept Sci & Humanities Srinagar 246174 Uttarakhand India

ISBN: (纸本)9789811004513;9789811004506

In this paper, a soft computing approach based on neural networks is presented for the numerical solution of a class of singular boundary value problems (SBVP) arising in physiology. The mathematical model of artificial neural network (ANN) is developed in a way to satisfy the boundary conditions exactly using log-sigmoid activation function in hidden layers. Training of the neural network parameters was performed by gradient descent backpropagation algorithm with sufficient number of independent runs. Two test problems from physical applications have been considered to check the accuracy and efficiency of the method. Proposed results for the solution of SBVP have been compared with the exact analytical solution as well as the solution obtained by the existing numerical methods and shows good agreement with others.

关键词： Neural networks Singular boundary value problem gradient descent algorithm

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