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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Guo, Xin-Chen Chen, Zhou-Yi Teng, Li-Rong Wu, Chun-Guo Du, Tian-Bao Lu, Jia-Hui Meng, Qing-Fan Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Science NortheastChinaInstituteofElectricPowerEngineering Jilin 132012 China Institute of General Development and Evaluation of Equipment EAAF PLA Beijing 100076 China College of Life Science Jilin University Changchun 130012 China

ISBN: (纸本)078039092X

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation. © 2005 IEEE.

关键词： Medicine

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Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, Hong Wan, Li.-Ming Jiang, Jing-Qing Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China

ISBN: (纸本)078039092X

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a Radial Basis Function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs. © 2005 IEEE.

关键词： Neural networks

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Approximated non-tensor pre-wavelets neural network based on compound training algorithm

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Journal of Information and computational Science 2005年第4期2卷 715-722页

作者： Li, Ying Deng, Zhidong Zou, Xi Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130021 China Institute of Mathematics Jilin University Changchun 130021 China

In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.

关键词： Neural networks

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Medicine composition concentration analysis based on least square support vector machine

Medicine composition concentration analysis based on least s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Xin-Chen Guo Zhou-Yi Chen Li-Rong Teng Chun-Guo Wu Tian-Bao Du Jia-Huilu Qing-Fan Meng Yan-Chun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun China College of Science Northeast China Institute of Electric Power Engineering Jilin China EAAF of PLA Institute of General Development and Evaluation of Equipment Beijing China College of Life Science Jilin University Changchun China

关键词： Least squares methods Support vector machines Educational institutions Performance analysis Algorithm design and analysis knowledge engineering Linear regression Spectral analysis Quadratic programming Lagrangian functions

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Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong Zheng Li-Ming Wan Jing-Qing Jiang Yan-Chun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing China College of Mathematics and Computer Science Inner Mongolia University for the Nationalities Tongliao China

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a radial basis function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs.

关键词： Powders Radial basis function networks Clustering algorithms Educational institutions Computer science Drugs Spectroscopy Educational technology knowledge engineering Artificial neural networks

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Optimal partition algorithm of RBF neural network and its applications to stock price prediction

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Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2005年第3期18卷 374-379页

作者： Sun, Yan-Feng Liang, Yan-Chun Zhang, Wen-Li Lu, Ying-Hua Key Laboratory for Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Department of Computer Science Northeast Normal University Changchun 130024 China

The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics of the RBF neural network. The approach for determining the centers and widths of the clustering is added in the modified OPA and applied to choose the centers and widths of the neural network. A method for adjusting the structure of the neural network dynamically is presented by using the difference of the objective functions of the clustering. Thus it is realized to select the number of the hidden nodes adaptively. Simulation results of the stock price prediction demonstrate the effectiveness of the proposed approach. Comparisons with traditional algorithms show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the algorithm combining the OPA with the orthogonal least squares (OLS) possesses more superior performance in the rightness of forecasting trends.

关键词： Optimal partition algorithm (OPA) Ordered sample Radial basis function (RBF) neural network Stock price prediction

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Solving constrained traveling salesman problems by genetic algorithms

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自然科学进展(英文版) 2004年第7期14卷 631-637页

作者： WU Chunguo LIANG Yanchun LEE Heowpueh LU Chun LIN Wuzhong College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education of China Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore

Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches based on modified genetic algorithms (GA) for solving these constrained TSPs are presented. Numerical experiments demonstrate that the algorithm for the open route TSP shows its advantages when the open route is required, the algorithm for the end-fixed TSP can deal with route optimization with constraint of fixed ends effectively, and the algorithm for the path-constraint could benefit the traffic problems where some cities cannot be visited from each other.

关键词： constrained traveling salesman problem genetic algorithm Hamiltonian path open route fixed end

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A hybrid algorithm for combining forecasting based on AFTER-PSO

A hybrid algorithm for combining forecasting based on AFTER-...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

作者： Feng, Xiaoyue Liang, Yanchun Sun, Yanfeng Lee, Heow Pueh Zhou, Chunguang Wang, Yan Key Laboratory of Symbol Computation and Knowledge Engineering Juin University Ministry of Education Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore

ISBN: (纸本)3540228179

A novel hybrid algorithm based on the AFTER (Aggregated forecast through exponential re-weighting) and the modified particle swarm optimization (PSO) is proposed. The combining weights in the hybrid algorithm are trained by the modified PSO. The linear constraints are added in the PSO to ensure that the sum of the combining weights is equal to one. Simulated results on the prediction of the stocks data show the effectiveness of the hybrid algorithm. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Algorithms

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A modified integer-coding genetic algorithm for job shop scheduling problem

A modified integer-coding genetic algorithm for job shop sch...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004

作者： Wu, Chunguo Xiang, Wei Liang, Yanchun Lee, Heow Pueh Zhou, Chunguang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun130012 China Institute of High Performance Computing Singapore117528 Singapore

ISBN: (纸本)3540228179

An operation template is proposed in this paper for describing the mapping between operations and a subset of natural numbers. With such operation template, a job shop scheduling problem (JSSP) can be transformed into a traveling salesman problem (TSP), hence the integer-coding genetic algorithm for TSP can be easily applied and modified. A decoding strategy, called virtual job shop, is proposed to evaluate the fitness of the individual in GA population. The integration of the operation template and virtual job shop makes the existing integer-coding GA possible for solving an extension of a classical job shop scheduling problem. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Traveling salesman problem

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Study on optimization of agent initial positions in land combat simulation

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自然科学进展(英文版) 2004年第3期14卷 257-261页

作者： WU Chunguo LIANG Yanchun LEE Heow Pueh LU Chun Yang Xiaowei College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Institute of High Performance Computing Singapore 117528 Singapore Institute of High Performance Computing Singapore 117528 Singapore Department of Applied Mathematics South China University of Technology Guangzhou 510640 China Centre for ACES Department of Mechanical Engineering National University of Singapore 119260 Singapore

The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that using the traditional SVM with the same precision. We also summarize and present some experiences and trends in the study on the optimization problem in land combat simulation.

关键词： multi-agent support vector machine radial basis function genetic algorithm regression

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