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检索条件"主题词=Gradient Descent Algorithm"

共 257 条记录，以下是211-220 订阅

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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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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...

引用

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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Supervisory Fuzzy Model Control for Magnetic Levitation System 13

Supervisory Fuzzy Model Control for Magnetic Levitation Syst...

引用

13th IEEE International Conference on Networking, Sensing, and Control (ICNSC)

作者： Su, Kuo-Ho Li, Chun-Yi Chinese Culture Univ Grad Inst Digital Mechatron Technol Taipei Taiwan

ISBN: (纸本)9781467399753

Magnetic levitation system (MLS), using the electromagnetic force to float, can effectively reduce the mechanical vibration friction and wearing loss caused by contact operation. However, the parameters in the mathematical model are related to the permanent magnet geometry, distance and the total mass of the platform. To achieve good tracking performance, a fuzzy system and the parameter identification algorithm for maglev suspension system are proposed in the paper. The proposed fuzzy model method is to establish a common fuzzy system procedure. By using the gathered data pairs, the parameters of membership functions are updated through gradient descent algorithm (GDA). In this paper, the fuzzy model system based on GDA is applied to ensure the effectiveness of tracking performance by replacing the mathematical model of MLS. Furthermore, to promote its performance under larger external disturbance, an auxiliary supervisory control effort is added. Thus, the chattering phenomenon is reduced and fast response can be obtained without degrading the tracking performance of the system dynamic. Some simulated and experimental results are provided to verify the feasibility and effectiveness of the proposed system.

关键词： Magnetic levitation system Fuzzy model system gradient descent algorithm Supervisory control

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Application of Multi-objective Artificial Immune Optimal algorithm in the Elevator Group Control System

Application of Multi-objective Artificial Immune Optimal Alg...

引用

3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Liu Yue-min Zhu Yan Henan Univ Sci & Technol Sch Elect Informat Engn Luoyang 471003 Peoples R China

ISBN: (纸本)9783037852866

With the development of the electronic technology, people have proposed higher requirements for the service quality on elevator, and the optimal elevator dispatching has developed a typical multi-objective optimal process. This paper analyzes both the advantages and the disadvantages of artificial immune algorithm and gradient descent algorithm, optimizes artificial immune algorithm, then proposes a novel optimal hybrid algorithm;at the same time, uses this hybrid algorithm in the elevator group control system combined with Pareto solution set. Making a comparison between the hybrid algorithm and the standard artificial immune algorithm, it's clear that this hybrid algorithm has certain feasibility and superiority, and to some extent, has improved the overall performance and service quality of the elevator group control system. This paper has provided a new method and a new thought on determination of the multi-objective weighted values in the elevator group control system.

关键词： Artificial Immune optimal algorithm gradient descent algorithm multi-objective optimization elevator group control system Pareto solution set

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Control Implementation of Compliant Composite Material Actuators for Wearable Robotic Exoskeleton 14

Control Implementation of Compliant Composite Material Actua...

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14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR)

作者： Dinh, Binh Khanh Cappello, Leonardo Masia, Lorenzo Nanyang Technol Univ Sch Aerosp & Mech Engn Singapore 639798 Singapore Ist Italian Tecnog Dept Robot Brain & Cognit Sci Genoa Italy

ISBN: (纸本)9781479918089

In previous work [1], [2], a novel actuator is presented that merges traditional electromechanical motors and multistable composite structures. It has been shown that these structures are able to arrange themselves in multi-stable configurations corresponding to local minima of their strain enery. When this composite structure is connected with the electromechanical motor as proposed, the resulting actuator shows significant benefits in terms of safety, energy saving and control implementation using the compliant property of the overall structure, the particular shape of the strain energy landscape, and the accurately predictable non-linear behavior. Hence the proposed actuator is well-suited for many robotic applications requiring continuous assistance and robust stability. In this paper, the structure's multistability property is exploited for energy saving purpose. In order to do that, a supervised learning method named Extreme Learning Machine is introduced to approximate the elastic force applied by the structure and gradient descent algorithm is used to compute the local minimum points equivalent to local minima of structure's strain energy.

关键词： Multistable Composite Transmission Compliant Actuator Extreme Learning Machine gradient descent algorithm

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An Adaptive Neural Network for Email Spam Classification 15

An Adaptive Neural Network for Email Spam Classification

引用

15th International Conference on Information Processing (ICINPRO) - Internet of Things

作者： Kumar, Jitendra Santhanavijayan, A. Rajendran, Balaji Bindhumadhava, B. S. NIT Trichy Ctr Develoment Adv Comp Bengaluru India Natl Inst Technol Trichy India Ctr Develoment Adv Comp Bengaluru India

ISBN: (纸本)9781728138077

In this paper, we propose an adaptive learning rate algorithm for gradient descent method used in Artificial Neural Network (ANN). The proposed adaptive learning rate neural network algorithm makes use of an initial assigned learning rate and after a set of backpropagation rounds the learning rate gets revised to a learning rate which is equal to minimum learning rate plus average of minimum learning rate and maximum learning rate (these parameters are updated in each recursive calls). During the next recursive call of the algorithm, the revised learning rate is used, if the average total error decreases, or the previous learning rate is continued. This is done until the desired learning rate is achieved which results in optimal weight parameters with high classification accuracy. We have compared the proposed algorithms with existing heuristics approaches and found that the convergence rate is to be faster and the proposed algorithm returns the optimized weights with higher classification accuracy. We have also tested the effectiveness of the algorithm on spam email classification using multilayer neural networks and found that it performs better than the existing approach and less prone to overfitting problems. The proposed algorithm results in 99.12% accuracy for the email classification.

关键词： Email Spam Classification gradient descent algorithm Learning Rate Machine Learning MLP (Multi-layer perceptron) Neural Network

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Using Particle Swarm Optimization in Training Neural Network for Indoor Field Strength Prediction

Using Particle Swarm Optimization in Training Neural Network...

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51st International Symposium ELMAR

作者： Vilovic, Ivan Burum, Niksa Milic, Dorde Univ Dubrovnik Dubrovnik Croatia

ISBN: (纸本)9789537044107

This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training a neural network, so we compared BP training algorithms: gradient descent method and Levenberg- Marquardt algorithm with PSO algorithm. PSO algorithm has been shown as powerful method for global optimization in several applications. A floor of university building in Dubrovnik has been used as case for simulation and measurement of signal strength. The results show that the neural network weights converge faster with PSO than with standard BP algorithms.

关键词： Neural network Multilayer Perceptron Training algorithm Backpropagation algorithm gradient descent algorithm Levenberg-Marquardt algorithm Particle swarm optimization

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ENERGY MINIMIZATION-BASED MIXTURE MODEL FOR IMAGE SEGMENTATION

ENERGY MINIMIZATION-BASED MIXTURE MODEL FOR IMAGE SEGMENTATI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao, Zhiyong Adel, Mouloud Bourennane, Salah Ecole Cent Marseille Inst Fresnel F-13013 Marseille France Univ Aix Marseille Inst Fresnel F-13013 Marseille France

ISBN: (纸本)9781479903566

A novel mixture model with spatial constraint is proposed for image segmentation. This model assumes that the pixel label prior probabilities are similar if the pixels are geometric close. An energy function is defined on the spatial space for measuring the spatial information. We also derive an energy function on the observed data space from the log-likelihood function of the standard mixture model. We estimate the model parameters and posterior probability by minimizing the combination of the two energy functions, using the gradient descent algorithm. Numerical experiments are presented where the proposed method is tested on synthetic and real world images. These experimental results demonstrate that the proposed method achieves competitive performance compared to spatially variant finite mixture model.

关键词： Energy minimization mixture model spatial information gradient descent algorithm image segmentation

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Event-triggered secure state estimation under sparse sensor attacks 41

Event-triggered secure state estimation under sparse sensor ...

引用

第41届中国控制会议

作者： An-Yang Lu Guang-Hong Yang College of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is concerned with the secure state estimation problem for a class of cyber-physical systems(CPSs) with multiple sensors equipped with a decentralized event-triggering(ET) *** with the traditional secure state estimators,the introduction of ET mechanism mitigates the unnecessary data transmission and brings more useful information for secure state *** make full use of the introduced event-triggering conditions which are not corrupted by the adversary,a novel data consolidation algorithm is proposed to consolidate the collected ***,a necessary and sufficient condition for the robust observability of the CPSs equipped with ET mechanism under sparse sensor attacks is provided,and a switched gradient descent algorithm is proposed to estimate the system state from the consolidated *** is shown that through consolidating the collected information,introducing ET mechanism helps improve the resilience against sparse sensor ***,a numerical simulation is provided to illustrate the effectiveness of the proposed algorithms.

关键词： Cyber-physical systems secure state estimation event-triggering mechanism gradient descent algorithm

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Learning and Perturbation based Multi-stage Nonlinear Pre-compensation algorithm in Coherent Optical Systems

Learning and Perturbation based Multi-stage Nonlinear Pre-co...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Wang, Yuheng Liu, Tengyuan Zhou, Junhe Tongji University Shanghai China

A learning and perturbation based multi-stage pre-compensation method is proposed. The nonlinear compensation performance is enhanced by introducing amplitude and phase factors in each stage of pre-compensation, which... 详细信息

ISBN: (纸本)9798350379266

关键词： fiber nonlinearity gradient descent algorithm optical transmission perturbation theory

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