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23rd Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Houhat, Nesrine Menigot, Sebastien Boutkedjirt, Tarek Drai, Redouane Girault, Jean-Marc Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Eseo Grp Angers France CNRS LAUM Le Mans Iniversite UMR 6613 Le Mans France USTHB Fac Phys Phys Ultrasound Res Team Algiers Algeria

ISBN: (纸本)9783030134686;9783030134693

The field of ultrasonic nondestructive testing has known a great development during the recent years. In order to increase the flaw detection sensitivity, many improvements have been made in the equipment and the sensors technology. In the present work, the optimal command which maximizes the flaw detection is investigated experimentally. A parametric optimization consisting of finding the optimal excitation frequency which maximizes the Euclidean distance between a reference medium and a medium with a linear flaw has been obtained automatically by using the gradient descent algorithm. Moreover, the waveform excitation optimization has been considered. A set of stochastic signals have been transmitted to the medium. A closed loop optimization process based on a genetic algorithm allowed to find the optimal excitation without a priori knowledge on the shape of the signal. This optimal excitation converged to a sinusoidal pulse with the optimal frequency found by the parametric optimization.

关键词： Optimal command Nondestructive testing gradient descent algorithm Genetic algorithm Ultrasound

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A PSO algorithm with Multi-Grouping and Two-Layer Structure 3

A PSO Algorithm with Multi-Grouping and Two-Layer Structure

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3rd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Zeng, Tiaojun Wang, Chuanjian Wei, Zhangliang Shihezi Univ Comp Dept Shihezi Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Peoples R China

ISBN: (纸本)9781728146447

A PSO (particle swarm optimization) algorithm with multiple groups and two-layer structure(MTPSO) is proposed. First, the whole particle swarm is divided into several groups, each one is optimized. For enhance efficiency in the optimization process, each particle adopts a mixed algorithm of negative gradient. Secondly, according to the function value of each particle solution, the particle group is divided into two structures: the elite layer and the ordinary layer. In the algorithm initialization phase, the particles with the best value in each group are selected into the elite layer. The particles in the elite layer are relatively fixed and regularly selected (similar to the parliamentary mechanism in human society), that is, each elite layer as a whole. After several iterations of optimization calculations in the same way as the normal layer, the new elite layer is formed again according to the previou method. The optimal solution calculated by the elite layer is added as a global optimal solution to the position update formula of each particle. Similarly, the optimal solution for the common layer is also added to the update formula for each particle in the elite layer. It reflects the upper and lower particle's mutual penetration and mutual influence of. Simulation experiments show the proposed algorithm is able to avoid premature and powerful global optimization ability and fast convergence, PSO's computing efficiency.

关键词： Particle Swarm Optimization Multiple grouping Two-layer structure gradient descent algorithm

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An Adaptive Neural Network for Email Spam Classification 15

An Adaptive Neural Network for Email Spam Classification

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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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A DATA-DRIVEN FAULT DETECTION APPROACH WITH PERFORMANCE OPTIMIZATION

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2018年第2期96卷 507-514页

作者： Li, Linlin Ding, Steven X. Peng, Kaixiang Han, Huayun Yang, Ying Yang, Xu Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst AKS D-47057 Duisburg Germany Peking Univ Dept Mech & Engn Sci Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

This paper is concerned with the data-driven realization of fault detection approach with performance optimization. For our purpose, the datadriven realization form of linear kernel representations is studied first, which is essential in our work. It is followed by a data-driven realization of kernel representation and its implementation in the design scheme of fault detection systems. Nevertheless, the basic idea behind this approach lies in the one-step identification of kernel representation using LQ-decomposition. Then, the recursive kernel representation is introduced and the so-called gradient descent algorithm is applied to optimize the performance of the proposed data-driven fault detection system. The effectiveness of the proposed approaches is illustrated by a numerical example and a case study on laboratory setup of a three-tank system.

关键词： data-driven methods observer-based fault detection gradient descent algorithm

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Form-finding method for multi-mode tensegrity structures using extended force density method by grouping elements

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COMPOSITE STRUCTURES 2018年 187卷 1-9页

作者： Cai, Jianguo Wang, Xinyu Deng, Xiaowei Feng, Jian Southeast Univ Natl Prestress Engn Res Ctr Minist Educ Key Lab C & PC Struct Nanjing 210096 Jiangsu Peoples R China Univ Hong Kong Dept Civil Engn Hong Kong Hong Kong Peoples R China

Multistable tensegrity structures are an intriguing form of compliant prestressed structures. Due to their attractive properties, these structures are attractive for a wide range applications. This papers aims the form-finding problem of tensegrity structures with multiple equilibrium modes. An optimization method for form-finding of multi-mode tensegrity structures is applied and then an equivalent optimization problem of energy-based objective function with Lagrange multiplier, regarded as an extension of the original force density method, is established. After the structural elements are grouped according to the property of symmetry, the objective function is minimized by the gradient descent algorithm, and as a result, the lengths of cable as well as the nodal coordinates are obtained and different structural modes corresponding to different grouping conditions can be achieved. Finally, several different modes in different grouping conditions of a planar and a spatial tensegrity structure have been obtained to verify the efficiency of proposed method.

关键词： Form-finding Multi-mode Energy method gradient descent algorithm Grouping

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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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Listwise approach based on the cross-correntropy for learning to rank

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ELECTRONICS LETTERS 2018年第14期54卷 878-879页

作者： Wu, Mintao Zhu, Jihua Wang, Jun Pang, Shanmin Li, Yaochen Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Jiangnan Univ Sch Digital Media Wuxi 214122 Peoples R China

The problem of learning to rank is addressed and a novel listwise approach by taking document retrieval as an example is proposed. It first introduces the concept of cross-correntropy into learning to rank and then proposes the listwise loss function based on the cross-correntropy between the ranking list given by the label and the one predicted by training model. The use of the cross-correntropy loss leads to the development of the listwise approach called ListCCE, which employs the gradient descent algorithm to train a neural network model. Experimental results tested on publicly available data sets show that the proposed approach performs better than some existing approaches.

关键词： information retrieval learning (artificial intelligence) entropy gradient methods listwise approach cross-correntropy loss learning to rank document retrieval listwise loss function training model ListCCE gradient descent algorithm neural network model

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Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing

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SENSORS 2019年第21期19卷 4666-4666页

作者： Li, Zhidu Liu, Hailiang Wang, Ruyan Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Key Lab Opt Commun & Networks Chongqing 400065 Peoples R China Key Lab Ubiquitous Sensing & Networking Chongqing 400065 Peoples R China

Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.

关键词： Mobile Crowd Sensing task assignment service benefit users clustering gradient descent algorithm

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

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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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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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