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Particle swarm optimization for automatic parameters determination of pulse coupled neural network

Journal of computers 2011年第8期6卷 1546-1553页

作者： Xinzheng, Xu Shifei, Ding Zhongzhi, Shi Zuopeng, Zhao Hong, Zhu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Pulse coupled neural network (PCNN), a wellknown class of neural networks, has original advantage when applied to image processing because of its biological background. However, when PCNN is used, the main problem is that its parameters aren't self-adapting according to different image which limits the application range of PCNN. Considering that, this paper proposed a new method based on particle swarm optimization (PSO) to determine automatically the parameters of PCNN. In this method, the algorithm of PSO is applied to search automatically optimum in the solution space of PCNN's parameters until finding global optimal solution. Experimental results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is better than the methods of Otsu, manual adjustment of parameters when mutual information is adopted as evaluation criteria. © 2011 ACADEMY PUBLISHER.

关键词： Image segmentation

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A rough RBF neural networks optimized by the genetic algorithm

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Advances in information Sciences and Service Sciences 2011年第7期3卷 332-339页

作者： Ding, Shifei Ma, Gang Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China

The large-scale data parallelism processing is an inherent characteristic of artificial neural networks, but the networks bring the efficiency problems of data processing. As one of the artificial neural networks, Radial Basis Function (RBF) neural networks have the same problem. Therefore, how to reduce the scale of data to improve the efficiency of data processing has been a hot issue among the artificial intelligence scholars. Based on the traditional RBF neural networks, this paper puts forward a method which determines the important degree of the sample attributes based on knowledge entropy of Rough set by analyzing the relationship between the knowledge entropy and the weight of the sample attributes, and assesses the importance of the sample attributes between the input layer and the hidden layer, namely the attribution reduction, so as to achieve reduce the scale of data processing. The ultimate aim of training RBF neural networks is to seek a set of suitable networks parameters which makes the sample output error achieve the minimum or required accuracy, while Genetic Algorithm (GA) has the properties of finding out the optimal solution through multiplepoint random search in the solution space, so Genetic Algorithm is used to optimize the centers, the widths and the weights between the hidden layer and the output layer of RBF neural networks in training the networks. Finally, a model about A Rough RBF Neural Networks Optimized by the Genetic Algorithm (GA-RS-RBF) is proposed in this paper. The simulation results show that the rough RBF neural network optimized by the Genetic Algorithm is better than the traditional RBF neural networks in classification about Iris datasets.

关键词： Genetic algorithms

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Zhou, Xionghui Liu, Juan Liu, Changning Rayner, Simon Liang, Fengji Ju, Jingfang Li, Yinghui Chen, Shanguang Xiong, Jianghui School of Computer Science Wuhan University Wuhan China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences Wuhan China Department of Pathology Stony Brook University Medical Center NY United States

ISBN: (纸本)9781457716669

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context). The microarray gene expression profile of brain tumors from 356 patients (The Cancer Genome Atlas dataset) was converted into a CoMi activity pattern, and showed significant positive correlation with the corresponding miRNA expression pattern. In a breast cancer cohort, the differential CoMi activity between good prognosis (longer survival) vs. bad prognosis patients forms a scale-free network, which highlighted a group of important cancer-related microRNAs and GO terms, e.g. hsa-miR-34a and 'cell adhesion'. Then two breast cancer cohorts were used in outcome prediction in an independent test. Using a popular T-test feature selection method and a support vector machine (SVM) classifier with 10-fold cross-validation, the CoMi activity feature achieves an area under curve (AUC) of 0.7155, better than the AUC value of 0.6339 for feature selection based on mRNA expression. In an independent test, CoMi feature selection achieved an AUC of 0.6874. Survival analysis also shows signatures defined by CoMi activity was predictive of survival and superior to mRNAs signatures. In short, we have demonstrated the first interrogation of dynamic remodeling of context specific miRNAs regulation networks in cancer. The altered microRNAs regulation on specific contexts could be used to predi

关键词： Diseases

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China's grain security warning based on multifactor information fusion

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2011年第5期27卷 183-189页

作者： Su, Xiaoyan Zhang, Huijie Li, Zhiqiang Deng, Yong School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Digital Agricultural Early-warning Technology Ministry of Agriculture of China Beijing 100081 China College of Computer and Information Sciences Southwest University Chongqing 400715 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China

China is the largest food consumption country in the world. With the social and economic development, China's food security has become a global attention. Grain security research involves many uncertain factors: as well as quantitative and qualitative information. In order to get the grain security status comprehensively, we proposed a method to evaluate risk in grain security based on multifactor information fusion. In the method, the quantitative and qualitative information were used to construct the basic probability assignment, and the attribute weights was got based on the Analytic Hierarchy Process method. After that, the multifactor fusion results were got based on the Dempster combination rule. The effectiveness of the method was verified with a numeric example that the data comes from the yearbook of China in 2007. The Results show that the method is effective and can correctly reflect the grain safety warning degrees.

关键词： Analytic hierarchy process

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A Kind of Wireless MAC Protocol to Improve Survivability of WSN

A Kind of Wireless MAC Protocol to Improve Survivability of ...

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International Symposium on Knowledge Acquisition and Modeling, KAM

作者： Yin Kai Yang Xiong School of Computer &Information Engineering Key Laboratory of Changzhou Software Technology Research and Application Changzhou Institute of Technology Changzhou Jiangsu China

In Wireless Sensor Network (WSN), the ability to avoid collision directly impacts on node energy consumption and network performance. Based on introduction of CSMA/CA protocol, the paper laid emphasis on shortcoming of binary back off algorithm affect survivability time of WSN. On this basis, a kind of CSMA/CA protocol based on improved binary back off algorithm was proposed. The algorithm reasonably scheduled competition and back off time of nodes and improved fairness of the network to increase network survival cycle. Network simulation results shows that the improved protocol effectively improves network survivable cycle in case of fixed network size. The performance of network delay and throughput also improved to a certain extent.

关键词： Media Access Protocol Wireless sensor networks Delay Multiaccess communication Energy consumption Collision avoidance

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Double-Row Cascade Labeling Algorithm for Hyper-Scale Issue

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Procedia Engineering 2011年 15卷 4047-4051页

作者： Yebin Fan Hualong Zhao Department of Computer Science Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

An efficient algorithm is presented to label the connected components in the case that the primary memory is smaller than the image data. Our algorithm uses only the memory of two image rows to label the huge image or any image larger than the available memory. The search path compression is a applied for improving the performance further. An extensive comparison with the state-of-art algorithms is proposed, both on random and real datasets. Our algorithm shows an impressive speedup, while the auxiliary memory is not required at all comparing with all competitors.

关键词： Connected components labeling page fault hyper-scalar union-find

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Research and comparison of granularity cluster analysis methods

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International Journal of Advancements in Computing technology 2011年第7期3卷 154-159页

作者： Ding, Shifei Tong, Chang School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

As an important component of data mining, Cluster Analysis (CA) has being attached importance to artificial intelligence, machine learning and other fields. Traditional clustering methods have been studied for a relatively long time;their technologies are mature and consequently they are well-applied. However, they are insufficient in clustering accuracy, noise sensitivity, along with effect on mass of data and non-convex clustering. Granular computing, which is regarded as a label of theories, methodologies, techniques, and tools, is an emerging conceptual and computing par information processing. It plays an important role information processing for fuzzy, uncertainty, partial truth and soft computing and is one of the main study stream in A.I. This paper introduces some new clustering methods, such as Fuzzy clustering, Clustering Algorithm Based on Rough Set, and clustering algorithm based on quotient space theory, emphatically expounds the basic thought and typical algorithms of these methods, and comparative analysis is carried out among these methods. Finally, new clustering algorithms are prospected and we put forward the value of research direction.

关键词： Data mining

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A feature selection algorithm based on tolerant granule

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Journal of Convergence information technology 2011年第1期6卷 191-195页

作者： Ding, Shifei Zhang, Yu Xu, Li Qian, Jun School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

The bottleneck problem has emerged in feature selection when processing high-dimension and large-scale data, so in the past decade, the researches on feature selection have not adhere to the traditional algorithms and ideas, showing a new trend of combining many new mathematical tools, which opens new space for feature selection applied in pattern recognition and makes further development in knowledge discovery and data mining. Granular computing has begun to take shape and show effect as a new idea of intelligent information processing, which creates the conditions for feature selection applied in data. The paper describes a new feature selection algorithm, basing on granular computing and making rough set approximation as background, the algorithm generates the granules, using a tolerance function, distinguishes noise data and inconsistent data, to achieve feature selection in the information table, and be effective for large-scale data sets.

关键词： Feature Selection

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Rough neural networks: A review

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Journal of Computational information Systems 2011年第7期7卷 2338-2346页

作者： Ding, Shifei Chen, Jinrong Xu, Xinzheng Li, Jianying School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

The rough neural networks (RNNs) are the neural networks based on rough set and one kind of hot research in the artificial intelligence in recent years, which synthesize the advantage of rough set to process uncertainly question: attributes reduce by none information losing then extract rule, and the neural networks have the strongly fault tolerance, self-organization, massively parallel processing and self-adapted. So that RNNs can process the massively and uncertainly information, which is widespread applied in our life. This article summarizes the recent research development of RNNs. First introduce the theory of rough set and the rough neuron;next analyze the RNNs in following four aspects: the neural network based on using rough set in preprocessing information, the neural networks base on rough logic, the neural networks based on rough neuron and the neural networks based on rough-granular;then give the flow chart of the RNNs processing question and the application of the classical neural network based on rough set;last give some advice to the development of RNNs in the future. Copyright © 2011 Binary information Press.

关键词： Rough set theory

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Adaptive sliding mode controller for vehicle suspension system

Adaptive sliding mode controller for vehicle suspension syst...

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International Conference on information Science and technology (ICIST)

作者： Mingyuan Xin Juntao Fei Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology College of Computer and Information Hohai University HHU Changzhou China

ISBN: (纸本)9781424494408

For the tracking control problem of vehicle suspension system, a method of adaptive sliding mode control is derive in this paper. The influence of parameter uncertainties and external disturbances on the system performance can be reduced and control robustness can be improved. The adaptive sliding mode controller is designed so that the practical system can track the state of the reference model. The asymptotically stability of the adaptive sliding mode control system is proved based on the Lyapunov stability theory. Numerical simulations demonstrate the effectiveness of the proposed adaptive sliding mode control.

关键词： Suspensions Adaptation model Sliding mode control Mathematical model Load modeling Vehicles Upper bound

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