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Support vector classifier based on principal component analysis

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Journal of Systems Engineering and Electronics 2008年第1期19卷 184-190页

作者： Zheng Chunhong jiao licheng Li Yongzhao School of Electronic Engineering Xidian Univ. Xi'an 710071 P. R. China

Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.

关键词： support vector classifier principal component analysis feature selection genetic algorithms

Distributed Parameter Neural Networks via Adaptive Wavelet

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Journal of Systems Engineering and Electronics 1993年第2期4卷 48-62页

作者： Zhuoer, Shi licheng, jiao Zheng, Bao Xidian Univ Xi'an China

The paper outlines a new neural net (DPNN) for describing brain's action based on one-dimensional cable theory. While the traditional neural network system only finding its character involving time changing (as HNN, BP etc.), the model-DPNNs (Distributed parameter neural networks) are not only the transmitted neurons of time variation, but also the functions of positions by the voltage u(x,t). With the neuroscientific relevance, some bioneural features like intermittent conduction and dendritic spike are fixed well by DPNNs which considered as complicated and adaptive devices contract to the functional elementary units. To find the semi-analytical representations of DPNNs, adaptive wavelets are utilized as new microlocalization tools. While maintaining all advantages of wavelet function, the adaptive wavelet offers a viable alternative learning procedure to the orthogonal least squares method (OLS), adaptive wavelet method develops a fairly general low-cost multiscale method for neural net optimization. In addition, a Lyapunov function method is also used to study the uniform asymptotic stability of DPNNs to support the new theory.

关键词： Brain Neurology Optimization

SAR Image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

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Chinese Journal of Electronics 2015年第1期24卷 205-211页

作者： CHANG Xia jiao licheng LIU Fang SHA Yuheng Institute of Image Processing and Understanding School of Mathematics and Information Science Beifang University of Nationalities Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China International Research Center for Intelligent Perception and Computation Xidian University School of Computer Science and Technology Xidian University

The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.

关键词： Synthetic aperture radar(SAR) image despeckling Stationary wavelet transform Nonsubsampled contourlet transform Scale mixtures of Gaussians model.

Optimal approximation of linear systems by artificial immune response

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Science in China(Series F) 2006年第1期49卷 63-79页

作者： GONG Maoguo DU Haifeng jiao licheng Institute of Intelligent Information Processing and Key Laboratory of Radar Signal Processing Xidian University Xi'an 710071 China School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 China

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

关键词： approximation of linear systems artificial immune systems immune response clonal selection,immunological memory

Reference direction based immune clone algorithm for many-objective optimization

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Frontiers of Computer Science 2014年第4期8卷 642-655页

作者： Ruochen LIU Chenlin MA Fei HE Wenping MA licheng jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi'an 710071 China

In this paper, a new preference multi-objective optimization algorithm called immune clone algorithm based on reference direction method （RD-ICA） is proposed for solving many-objective optimization problems. First, an intelligent recombination operator, which performs well on the functions comprising many parameters, is introduced into an immune clone algorithm so as to explore the potentially excellent gene segments of all individuals in the antibody pop- ulation. Second, a reference direction method, a very strict ranking based on the desire of decision makers （DMs）, is used to guide selection and clone of the active population. Then a light beam search （LBS） is borrowed to pick out a small set of individuals filling the external population. The proposed method has been extensively compared with other recently proposed evolutionary multi-objective optimization （EMO） approaches over DTLZ problems with from 4 to 100 objectives. Experimental results indicate RD-ICA can achieve competitive results.

关键词： many-objective optimization preference multiobjective optimization artificial immune system reference direction method light beam search intelligent recombination operator

FWNN for Interval Estimation with Interval Learning Algorithm

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Journal of Systems Engineering and Electronics 1998年第1期9卷 56-66页

作者： Wang, Ling Liu, Fang jiao, licheng State Key Lab of RSP and NN Research Center Xidian University Xi'an 710071 P. R. China

In this paper, a wavelet based fuzzy neural network for interval estimation of processed data with its interval learning algorithm is proposed. It is also proved to be an efficient approach to calculate the wavelet c... 详细信息

关键词： Fuzzy wavelet neural network (FWNN) Interval learning algorithm.

Image segmentation based on differential immune clone clustering algorithm

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International Journal of Intelligent Computing and Cybernetics 2013年第1期6卷 83-102页

作者： Wenping Ma Feifei Ti Congling Li licheng jiao Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian UniversityXi’anChina

Purpose–The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm(DICCA)to solve image ***/methodology/approach–DICCA combines immune clone selection and differential evolution,and two populations are used in the evolutionary *** reproduction and selection,differential mutation,crossover and selection are adopted to evolve two populations,which can increase population diversity and avoid local *** extracting the texture features of an image and encoding them with real numbers,DICCA is used to partition these features,and the final segmentation result is ***–This approach is applied to segment all sorts of images into homogeneous regions,including artificial synthetic texture images,natural images and remote sensing images,and the experimental results show the effectiveness of the proposed ***/value–The method presented in this paper represents a new approach to solving clustering *** novel method applies the idea two populations are used in the evolutionary *** proposed clustering algorithm is shown to be effective in solving image segmentation.

关键词： Differential evolution Clone selection Clustering Image segmentation Image processing Cluster analysis

Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Science in China(Series F) 2008年第8期51卷 1064-1082页

作者： GONG MaoGuo jiao licheng MA WenPing DU HaiFeng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

关键词： multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms

A memetic algorithm based on hyper-heuristics for examination timetabling problems

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International Journal of Intelligent Computing and Cybernetics 2015年第2期8卷 139-151页

作者： Yu Lei Maoguo Gong licheng jiao Yi Zuo Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and ComputationXidian UniversityXi’anChina

Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search ***-heuristic is a kind of representative *** hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global ***/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling ***,four graph coloring heuristics are employed to construct heuristic *** the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark ***/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.

关键词： Evolutionary computation Examination timetabling problem Hyper-heuristic Memetic algorithm