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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Kim, Chyon Hae Tsujino, Hiroshi Sugano, Shigeki Honda Res Inst Japan Co Ltd 8-1 Honcho Wako Saitama 3510188 Japan Waseda Univ Dept Mech Engn Shinjuku Ku Tokyo 1698555 Japan

ISBN: (纸本)9781457708398

This paper addresses optimal motion for general machines. Approximation for optimal motion needs a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that is composed of a path searching algorithm and a pruning algorithm. The pruning algorithm is based on our analysis for the resemblances of states. To confirm the precision, calculation cost, optimality, and applicability of the proposed algorithm, we conducted several shortest time path planning examinations for the dynamic models of double inverted pendulums. The precision to reach the goal state of the pendulums was better than other algorithms. The calculation was at least 58 times faster. There was a positive correlation between the optimality and the resolutions of the proposed algorithm. As a result of torque based feedback control simulation, we confirmed applicability of the proposed algorithm under noisy situation.

关键词： algorithm design and analysis Approximation algorithms Heuristic algorithms Joints Path planning Planning Torque approximation theory double inverted pendulum dynamic model feedback global path planning algorithm motion control noisy situation nonlinear systems optimal motion approximation optimal path planning algorithm path planning path searching algorithm pendulums phase space positive correlation pruning algorithm rapid approximation torque based feedback control simulation

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A Cognitive Approach for Attribute Selection in Internet Dataset 16

A Cognitive Approach for Attribute Selection in Internet Dat...

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IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Kaleem, Danish Ferens, Ken Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781538607718

With the evolution of internet, there has been an unprecedented and unlimited growth in volume, velocity, veracity and variety of the data and the complexity of data attributes is on the rise. Further, in the domain of internet, data is not geo-centric any longer and multiple locations are contributing to the data acquisition technologies including but not limited to packet captures, data logs, routing and switching technologies and security detection and prevention systems. It can be stated that internet data is highly sparse with high dimensionality and an event can be represented by correlation of multiple attributes of a data set. Notwithstanding, analysis of such data set takes enormous human efforts and time. Machine learning has been found as promising candidate to extract information of interest from the data set but human cognitive analysis is required to feed to learning algorithms which is also called data preprocessing stage. This analysis requires cognitive domain knowledge, experiential learning and complexity analysis and therefore, traditional models fail in selecting proper attributes due to nonexistence of cognitive aspects. In this work, authors have proposed a fractal based cognitive model for artificial neural network to extract important attributes from two different internet data sets.

关键词： Cognitive selection Artificial neural networks wireless data set Internet data set pruning algorithm Feature selection and elimination Box counting fractal dimension Dataset fields

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A Method to Determine the Parameters of Phase Space Reconstruction Based on the Neural Network

A Method to Determine the Parameters of Phase Space Reconstr...

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International Workshop on Chaos-Fractals Theories and Applications

作者： Liu, Runjie Hou, Zhan Shen, Jinyuan Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9780769538532

In classic phase space reconstruction, the time lag is identical. In our research, the different time lags are found more effectively for teletraffic forecasting. In this paper, a method to determine the different time lags in phase space reconstruction is proposed. Simulation results show that the prediction is more accurate by using the different time lags in reconstruction phase space.

关键词： Phase Space Reconstruction time lags embedding dimensions TDNN pruning algorithm

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A Fast Two-Stage Extreme Learning Machine 19

A Fast Two-Stage Extreme Learning Machine

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3rd International Conference on Deep Learning Technologies (ICDLT)

作者： Lai, Jie Wang, Xiaodan Li, Rui Gu, Jinghao Air Force Engn Univ Air & Missile Def Coll Xian Peoples R China

ISBN: (纸本)9781450371605

In order to realize the automatic adjustment of the network structure of the extreme learning machine (ELM), inspired by the two-stage extreme learning machine (TS-ELM), a fast two-stage extreme learning machine (FTS-ELM) is proposed by making the nodes added follow the arithmetic progression and using the principal component analysis (PCA) for pruning the redundant nodes. In the growing stage of hidden nodes, the nodes are added into network according to the arithmetic progression to reduce the number of iterations. In the pruning phase, PCA is used to delete redundant nodes. The hidden nodes with low contribution rate are quickly reduced by continuously reducing the cumulative contribution rate threshold, until the error (accuracy) achieves its maximum (minimum), which makes the network structure more compact. The empirical studies show that compared with ELM, EM-ELM, OP-ELM and TS-ELM algorithms, FTS-ELM leads to a compact network structure with good generalization performance, and its training time is far less than TS-ELM.

关键词： Extreme Learning Machine Principal Component Analysis Neural Network Structure Growing algorithm pruning algorithm

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Study on Boiler Combustion Optimization Based on Sparse Least Squares Support Vector Machine 8

Study on Boiler Combustion Optimization Based on Sparse Leas...

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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Chen, Nankun Lv, Jianhong Southeast Univ Sch Energy & Environm Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467395878

Under the background of seeking high efficiency and low nitrogen oxides (NOx) emissions for the boiler of power plants, this paper used least squares support vector machine (LSSVM) to model the boiler efficiency and NOx emissions of a power plant according to the experimental data acquired from a combustion adjustment test. A pruning algorithm based on active learning was applied to the combustion model built earlier to obtain a sparse LSSVM model. Compared to Suykens standard pruning algorithm for LSSVM, AL-LSSVM (active learning LSSVM) can significantly reduce the complexity of combustion models without degrading much, which provides an effective method for incremental or adaptive learning of combustion models.

关键词： combustion optimization least squares support vector machine active learning pruning algorithm

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Structural learning with M-apoptosis in neurofuzzy GMDH

Structural learning with M-apoptosis in neurofuzzy GMDH

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IEEE International Conference on Fuzzy Systems at the World Congress on Computational Intelligence (WCCI 98)

作者： Ohtani, T Ichihashi, H Miyoshi, T Nagasaka, K Univ Osaka Prefecture Sakai Osaka 591 Japan

ISBN: (纸本)078034863X

There have been many studies of mathematical models of neural networks. However there always arises a problem of determining their optimal structures because of the lark of prior information. Apoptosis is the mechanism responsible for the physiological deletion of cells and appears to be intrinsically programmed. We propose a procedure named M-apoptosis for the structure clarification of Neurofuzzy GMDH model whose partial descriptions are represented by the Radial Basis functions network. The proposed method prunes unnecessary links and units hom the larger network to identify, still more to clarify the network structure by minimizing the Minkowski norm of the derivatives of the partial descriptions. The method is validated in the numerical examples of function approximation and the classification of Fisher's Iris data.

关键词： pruning algorithm neurofuzzy RBF network GMDH

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Density based pruning for identification of differentially expressed genes from microarray data

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BMC GENOMICS 2010年第2-Sup期11卷 S3-S3页

作者： Hu, Jianjun Xu, Jia Univ S Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

Motivation: Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results: We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB pruning) is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO) with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions: Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://***/degprune

关键词： Area Under Curve Microarray Dataset Average Expression Level pruning algorithm Rank Product

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On k-Path Covers and their applications 40th

On <i>k</i>-Path Covers and their applications

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40th International Conference on Very Large Data Bases

作者： Funke, Stefan Nusser, Andre Storandt, Sabine Univ Stuttgart FMI Univ Str 38 D-70569 Stuttgart Germany Univ Freiburg Georges Kohler Allee 51 D-79110 Freiburg Germany

For a directed graph G with vertex set V, we call a subset a k-(All-)Path Cover if C contains a node from any simple path in G consisting of k nodes. This paper considers the problem of constructing small k-Path Covers in the context of road networks with millions of nodes and edges. In many application scenarios, the set C and its induced overlay graph constitute a very compact synopsis of G, which is the basis for the currently fastest data structure for personalized shortest path queries, visually pleasing overlays of subsampled paths, and efficient reporting, retrieval and aggregation of associated data in spatial network databases. Apart from a theoretic investigation of the problem, we provide efficient algorithms that produce very small k-Path Covers for large real-world road networks (with a posteriori guarantees via instance-based lower bounds). We also apply our algorithms to other (social, collaboration, web, etc.) networks and can improve in several instances upon previous approaches.

关键词： Path cover Graph compression VC-dimension pruning algorithm Personalized route planning Spatial network database

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A Structural Optimization algorithm for Complex-Valued Neural Networks

A Structural Optimization Algorithm for Complex-Valued Neura...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Dong, Zhongying Huang, He Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

ISBN: (纸本)9781728124858

A suitable network structure can not only save the computing resources but also enhance the generalization ability of the constructed neural network. A pruning algorithm with group lasso regularization is proposed in this paper, where both the superfluous hidden and input neurons can be efficiently removed. Based on this algorithm, appropriate complex-valued neural networks (CVNNs) are designed for practical applications. It is noted that the numerical ill-posed problem would be caused by the nondifferentiability of the group lasso regularization at the origin. To overcome it, a smooth function is introduced to approximate the regularization term. Then, CVNNs with good generalization capability and reasonable network structure are obtained. Experimental results on some benchmark classification problems are provided to show the performance of the proposed pruning algorithm.

关键词： pruning algorithm hidden and input nodes smooth group lasso regularization complex-valued neural networks

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Research on aircraft target assignment decision based on improved Monte Carlo method 35

Research on aircraft target assignment decision based on imp...

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35th Chinese Control and Decision Conference (CCDC)

作者： Cui, Yameng Wei, Bingyu Wen, Jiabao Zhao, Bo Li, Deqiang Beijing Aerosp Automat Control Inst Beijing Peoples R China

ISBN: (纸本)9798350334722

Modern warfare is developing towards systematic warfare. The cooperation of multiple aircraft can improve the overall combat capability, and the optimal attack effect can be obtained by optimizing the cooperative target assignment scheme. Cooperative target assignment can also he called weapon target assignment problem. To solve the problem of long search time and low search efficiency, an improved Monte Carlo Tree search algorithm is proposed. First of all, a dynamic cooperative target assignment algorithm is designed according to the optimized cooperative target assignment type, and each aircraft performs dynamic target assignment as the number of targets changes. Then aiming at the problem that the search time of Monte Carlo tree search algorithm is too long, the pruning algorithm is added to improve the search process of Monte Carlo, and the MCTS_prun algorithm with a shorter search time is designed. Finally, the original Monte Carlo tree search method and MCTS_prun algorithm in different confrontation scenarios are compared. It is verified that using the MCTS_prun algorithm to make target assignment decisions can save target assignment time while ensuring the optimal overall damage effect, and improve the search efficiency of MCTS algorithm.

关键词： Cooperative operation Target assignment Monte Carlo Tree Search pruning algorithm

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