2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第10页

EEG Absence Seizure Detection with Autocorrelation Function and Recurrent Neural Network 基于自相关函数和递归神经网络的脑电图缺失发作检测

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002853

Yifei Yu, Haoran Qin, Yuanxiang Li, Zaifen Gao, Z. Gai

引用次数: 1

Performance Study of Double-Niched Evolutionary Algorithm on Multi-objective Knapsack Problems 双小生境进化算法在多目标背包问题中的性能研究

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003130

Ryoya Osawa, Shinya Watanabe, T. Hiroyasu, S. Hiwa

引用次数: 1

A Neural Network for Constrained Fuzzy Convex Optimization Problems 约束模糊凸优化问题的神经网络

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002986

Na Liu, Han Zhang, Sitian Qin

引用次数: 1

Interpretability for Neural Networks from the Perspective of Probability Density 从概率密度角度看神经网络的可解释性

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002817

L. Lu, Tingting Pan, Junhong Zhao, Jie Yang

引用次数: 1

Research on Reconstruction Strategy of Closed Bus-ties Power Grid Based on Binary Particle Swarm Optimization 基于二元粒子群优化的封闭母线电网重构策略研究

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003038

Haozhe Liang, Xiwen Gong

引用次数: 2

A Distributed HALS Algorithm for Euclidean Distance-Based Nonnegative Matrix Factorization 基于欧氏距离的非负矩阵分解的分布式HALS算法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003158

Yohei Domen, T. Migita, Norikazu Takahashi

引用次数: 2

A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks 无线传感器网络中无人机数据采集的多目标超启发式算法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002862

Zhixing Huang, Chengyu Lu, J. Zhong

引用次数: 2

Niche Method Complementing the Nearest-better Clustering 互补最近邻聚类的小生境方法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002742

Yuhao Li, Jun Yu, H. Takagi

{"title":"Niche Method Complementing the Nearest-better Clustering","authors":"Yuhao Li, Jun Yu, H. Takagi","doi":"10.1109/SSCI44817.2019.9002742","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002742","url":null,"abstract":"We propose a two-stage niching algorithm that separates local optima areas in the first stage and finds the optimum point of each area using any optimization technique in the second stage. The proposed first stage has complementary characteristics to the shortcoming of Nearest-better Clustering (NBC). We introduce a weighted gradient and distance-based clustering method (WGraD) and two methods for determining its weights to find out niches and overcome NBC. The WGraD creates spanning trees by connecting each search point to other suitable one decided by weighted gradient information and weighted distance information among search points. Since weights influence its clustering result, we propose two weight determination methods 1 and 2. The weight determination method 1 firstly forms one spanning tree and then uses a dynamic pruning method and the Hill-Valley test to cut long edges and repair them. The weight determination method 2 assigns different weights to different search points based on distance information. We combine these methods into WGrad, i.e. WGraD1 and WGraD2, and compare the characteristics of NBC, WGraD1, and WGraD2 using differential evolution (DE) as a baseline search algorithm for obtaining the optimum of each niche after clustering local areas. We design a controlled experiment and run (NBC + DE), (WGraD1 + DE) and (WGraD2 + DE) on 8 benchmark functions from CEC 2015 test suite for single objective multiniche optimization. The experimental results confirmed that the proposed strategy can overcome the shortcoming of NBC and be a complementary niche method of NBC.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"87 1","pages":"3065-3071"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88521119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A New Improved Convolutional Neural Network Flower Image Recognition Model 一种新的改进卷积神经网络花卉图像识别模型

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003016

Min Qin, Yuhang Xi, Frank Jiang

{"title":"A New Improved Convolutional Neural Network Flower Image Recognition Model","authors":"Min Qin, Yuhang Xi, Frank Jiang","doi":"10.1109/SSCI44817.2019.9003016","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003016","url":null,"abstract":"In order to improve the accuracy of the flower image recognition, a convolutional neural network (A-LDCNN) model based on attention mechanism and LD-loss (Linear Discriminant Loss Function) is proposed. Unlike traditional CNN (Convolutional Neural Networks), A-LDCNN uses the VGG-16 network pre-trained by ImageNet to perform feature learning on preprocessed flower images. The attention feature is constructed by fusing the local features of the multiple intermediate convolution layers with the global features of the fully connected layer and using it as the final classification feature. LDA (Latent Dirichlet Allocation) is introduced into the model to construct a new loss function LD-loss, which participates in the training of CNN to minimize the feature distance in class and maximize the feature distance between classes, and to solve the problem of Inter-class similarity and intra-class difference in flower image classification. Classification experiments show that the accuracy of A-LDCNN is 87.6%, which is higher than other traditional networks and can realize the accurate recognition of flower images under natural conditions.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"3110-3117"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88235835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Noisy Zhang-Dynamics (ZD) Method for Genesio Chaotic (GC) System Synchronization: Elegant Analyses and Unequal-Parameter Extension Genesio混沌(GC)系统同步的噪声张动力学(ZD)方法:优雅分析与不等参数推广

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002889

Canhui Chen, Yihong Ling, Deyang Zhang, Nini Shi, Yunong Zhang

引用次数: 1