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9th International Conference on Machine Vision (ICMV)

作者： Siwar, Yahia Salwa, Said Olfa, Jemai Mourad, Zaied Chokri, Ben Amar Natl Sch Engn Sfax Sfax Tunisia

ISBN: (纸本)9781510611320

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feedforward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

关键词： Extreme learning machine Wavelet neural networks learning algorithm Classification feedforward neural network wavelet

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Parallel learning Portfolio-based solvers

Parallel Learning Portfolio-based solvers

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International Conference on Computational Science (ICCS)

作者： Menouer, Tarek Baarir, Souheib Paris Nanterre Univ CNRS UMR 7606 Lab LIP6 Paris France LRDE Lab Kremlin Bicltre France

Exploiting multi-core architectures is a way to tackle the CPU time consumption when solving SAT-isfiability (SAT) problems. Portfolio is one of the main techniques that implements this principle. It consists in making several solvers competing, on the same problem, and the winner will be the first that answers. In this work, we improved this technique by using a learning schema, namely the Exploration Exploitation using Exponential weight (EXP3), that allows smart resource allocations. Our contribution is adapted to situations where we have to solve a bench of SAT instances issued from one or several sequence of problems. Our experiments show that our approach achieves good results. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： Portfolio SATisfiability boolean learning algorithm Parallelization

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Diagnostic Neuro-Fuzzy System and Its learning in Medical Data Mining Tasks in Conditions of Uncertainty about Numbers of Attributes and Diagnoses

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2017年第6期51卷 391-398页

作者： Pliss, Iryna Perova, Iryna Kharkiv Natl Univ Radio Elect UA-61166 Kharkov Ukraine

Architecture and learning method for evolving diagnostic neuro-fuzzy-system for Medical Data Mining tasks in situation of uncertainty about quantities of attributes and diagnoses are proposed. Diagnostic neuro-fuzzy-system was approbated on data set, which present erosive ulcerous disease of the gastrointestinal tract and shown high quality of classification in condition of different quantity of input and output data.

关键词： Medical Data Mining classification fuzzification neuro-fuzzy system learning algorithm membership function

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Minimizing Base Stations' ON/OFF Switchings in Self-Organizing Heterogeneous Networks: A Distributed Satisfactory Framework

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IEEE ACCESS 2017年 5卷 26267-26278页

作者： Arani, Atefeh Hajijamali Omidi, Mohammad Javad Mehbodniya, Abolfazl Adachi, Fumiyuki Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Tohoku Univ Res Org Elect Commun Sendai Miyagi 9808577 Japan

The deployment of heterogeneous networks (HetNets) can significantly boost the network capacity. However, the large number of small cell base stations (SBSs) deployed in HetNets can result in an increased total energy consumption. One of the promising techniques to reduce the energy consumption of networks is base station (BS) ON/OFF switching (sleeping) approaches. Due to device lifetime and energy waste by unnecessary switchings, the number of switchings is considered as an important problem. In this paper, we formulate the ON/OFF switching problem as a satisfaction game, where BSs seek to meet certain performance constraints in order to avoid the frequent BS switchings. Furthermore, BSs can choose their transmission power levels according to the network conditions in a distributed manner. The proposed satisfaction game involves a multi-step process. In the first step, we aim at satisfying the players with the high satisfaction threshold in a predefined time interval. To measure a BS's satisfaction, a utility function is used that includes BS's load and power consumption, in which the load of each BS is coupled with the load of other BSs. Since all players cannot be simultaneously satisfied, unsatisfied players decide to reduce their thresholds, and form a game with the redefined thresholds. To solve the game, a regret-based satisfaction algorithm and a satisfaction equilibrium search algorithm are applied. Simulation results show that the proposed schemes can achieve significant reductions in the number of switchings compared with the benchmark methods.

关键词： Heterogeneous networks game theory learning algorithm self-organizing networks sleep mode

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Stacked-Structure-Based Hierarchical Takagi-Sugeno-Kang Fuzzy Classification Through Feature Augmentation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017年第6期1卷 421-436页

作者： Zhou, Ta Ishibuchi, Hisao Wang, Shitong Jiangnan Univ Sch Digital Media Wuxi 214122 Jiangsu Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Engn Zhenjiang 100083 Jiangsu Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Japan Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Osaka 5998531 Japan JiangSu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China

In this paper, a new stacked-structure-based hierarchical Takagi-Sugeno-Kang (TSK) fuzzy classifier called SHFA-TSK-FC with both promising performance and high interpretability is proposed to tackle with the shortcoming of the existing hierarchical fuzzy classifiers in interpreting the outputs and fuzzy rules of intermediate layers. In order to achieve the enhanced classification performance, each component unit, which is a zero-order TSK fuzzy classifier, in SHFA-TSK-FC is organized in a stacked way such that all the input features of the original training samples plus the interpretable augmented features, corresponding to the interpretable output of each previous component unit, are fed as the input features of the current component unit. These augmented features can essentially open the manifold structure of the original input space such that the enhanced classification performance can be expected. In designing each component unit, its analytical solution to the consequent parts of fuzzy rules therein is obtained quickly by using the least learning machine such that SHFA-TSK-FC becomes scalable for large datasets. Its high interpretability is guaranteed by randomly selecting the input features and randomly choosing the fixed five Gaussian membership functions for the selected input features in the premise of each fuzzy rule. Experimental results on real-life datasets and an application case demonstrate the enhanced or at least comparable classification performance and high interpretability of SHFA-TSK-FC.

关键词： Feature augmentation interpretability learning algorithm least learning machine stacked hierarchical structure TSK fuzzy classifier

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A Bird Flock Gravitational Search algorithm Based on the Collective Response of Birds

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COMPUTER JOURNAL 2017年第11期60卷 1687-1716页

作者： Han, XiaoHong Qiang, Yan Lan, Yuan Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan Shanxi Peoples R China

Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass which is widely used nowadays for efficient solution of optimization problems. For the purpose of enhancing the performance of original GSA, this paper proposes a new GSA called Bird Flock Gravitational Search algorithm (BFGSA) based on the collective response of birds. Although GSA performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main major steps including initialization, identification of the nearest neighbors and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original particle swarm optimization and state-of-the-art GSA algorithms. Furthermore, we applied BFGSA to a real-world application of data clustering. The results show that BFGSA improves the performance of the original GSA and obtains the best results compared with our selected GSA-type algorithms in benchmarking experiments and clustering experiments.

关键词： gravitational search algorithm learning algorithm heuristic search algorithm collective behavior numerical function optimization data clustering

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A novel data clustering algorithm based on modified gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 61卷 1-7页

作者： Han, XiaoHong Quan, Long Xiong, XiaoYan Almeter, Matt Xiang, Jie Lan, Yuan Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Sys Minist Educ China Taiyuan Shanxi Peoples R China

Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search algorithm is proposed, which is called Bird Flock Gravitational Search algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.

关键词： Gravitational search algorithm learning algorithm Collective behavior Data clustering Clustering Validation Nature-inspired algorithm

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Intelligent tracking control of a PMLSM using self-evolving probabilistic fuzzy neural network

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IET ELECTRIC POWER APPLICATIONS 2017年第6期11卷 1043-1054页

作者： Chen, Syuan-Yi Liu, Tung-Sheng Natl Taiwan Normal Univ Dept Elect Engn Taipei 106 Taiwan

This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.

关键词： intelligent control permanent magnet motors linear motors synchronous motors machine control position control machine vector control intelligent tracking control SPFNN self-evolving probabilistic fuzzy neural network asymmetric membership function controller permanent magnet linear synchronous motor servo drive system field-oriented control SPFNN-AMF control system PMLSM servo drive system control process learning algorithm structure learning parameter learning

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Exergetic Performance Prediction of a Roughened Solar Air Heater Using Artificial Neural Network

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2018年第3期64卷 195-206页

作者： Ghritlahre, Harish Kumar Prasad, Radha Krishna Natl Inst Technol Dept Mech Engn Rourkela India

The exergetic performance of a roughened solar air heater has been predicted using an artificial neural network (ANN) model. The exergetic values have been derived from the data collected by conducting experiments on solar air heater using transverse wire ribs as a roughness element on the absorber plate with relative roughness heights 0.009571, 0.01400, 0.017142 and constant relative roughness pitch 10, at local weather condition of Jamshedpur (22.77 degrees N and 86.14 degrees E), India. The ANN model was structured with seven input parameters such as experimental time, solar radiation intensity, roughness size, atmospheric temperature, mean air temperature, absorber plate temperature and mass flow rate of air in input layer, and five parameters such as exergy inlet, exergy outlet, exergy efficiency, exergy destruction and improvement potential in output layer. Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Polak-Ribiere conjugate gradient (CGP) learning algorithms have been used for training the proposed model. The LM learning algorithms with 7 neurons in the hidden layer are found to be optimal on the basis of statistical error analysis. The value of R-2 for the predicted model of exergy outlet, exergy inlet, exergetic efficiency, exergy destruction and improvement potential were 0.99584, 0.99997, 0.99517, 0.99997 and 0.99983 respectively, which yields the satisfactory performance of the ANN model. The values of mean square error (MSE), the coefficient of variance (COV), the and mean relative error (MRE) are found to be very low for predicted values of exergetic performance parameters that are desirable. The statistical results exhibit that the proposed multi-layered perceptron (MLP) ANN model successfully predicts the exergetic performance of a roughened solar air heater.

关键词： solar air heater exergy analysis artificial neural network learning algorithm multi-layer perceptron

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Heterogeneous network bandwidth management scheme based on the principal-agent game model

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WIRELESS NETWORKS 2017年第1期23卷 279-287页

作者： Kim, Sungwook Sogang Univ Dept Comp Sci Shinsu Dong 1 Seoul 121742 South Korea

In order to provide more comprehensive network services, a concept of integrated heterogeneous network system was introduced. Until now, lots of researchers have focused on how to efficiently integrate different types of wireless and mobile networks. To exploit the heterogeneous network system operation, an important issue is how to properly manage the network bandwidth. In this study, a new bandwidth management scheme has been proposed by employing the principal-agent game model. Among heterogeneous networks, we have analyzed the asymmetric information situation and developed an effective bandwidth allocation algorithm. Under diverse network condition changes, our principal-agent game approach is essential to provide a suitable tradeoff between conflicting requirements. Simulation results demonstrate that the proposed scheme can obtain a better network performance and bandwidth efficiency than other existing schemes.

关键词： Dynamic bandwidth allocation Principal-agent model Game theory Heterogeneous networks learning algorithm

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