Swarm Target Tracking Collective Behavior Control with Formation Coverage Search Agents & Globally Asymptotically Stable Analysis of Stochastic Swarm

According to the viewpoints of swarm dynamic, this paper elaborates that introducing the target tracking control and global asymptotic stability respectively of the two kinds of different swarm systems with aggregation functions, the study of multi-agent systems is start with two aspects: one hand is starting off from formation coverage search, but on the other hand, researching the character of high noise inhibiting ability and stability. By use of the swarm dynamical models, meanwhile, considering the interactions among agents, based on the artificial potential functions (APFs) and Newton-Raphson iteration updating behavior rules, theoretical analysis and simulation experimental studies results are presented to illustrate the performance and viability of the proposed algorithm which is robust with respect to the system uncertainties and additive disturbances.

[1]  M. Williams.  The stresses around a fault or crack in dissimilar media , 1959 .

[2]  David B. Bogy,et al.  Edge-Bonded Dissimilar Orthogonal Elastic Wedges Under Normal and Shear Loading , 1968 .

[3]  J. Bassani,et al.  Stress intensity factors in bonded half planes containing inclined cracks and subjected to antiplane shear loading , 1979, International Journal of Fracture.

[4]  G. Sinclair,et al.  On the stress singularities in the plane elasticity of the composite wedge , 1979 .

[5]  J G Walker.  The Geometry of Satellite Clusters. , 1981 .

[6]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[7]  M. Al-Baali.  Descent Property and Global Convergence of the Fletcher—Reeves Method with Inexact Line Search , 1985 .

[8]  T. C. T. Ting,et al.  Explicit solution and invariance of the singularities at an interface crack in anisotropic composites , 1986 .

[9]  Ma Chien-Ching,et al.  Analysis of dissimilar anisotropic wedges subjected to antiplane shear deformation , 1989 .

[10]  A. Dembo,et al.  High-order absolutely stable neural networks , 1991 .

[11]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  Ya-Xiang Yuan,et al.  Convergence properties of the Fletcher-Reeves method , 1996 .

[14]  Amir Reza Shahani,et al.  Analysis of an isotropic finite wedge under antiplane deformation , 1997 .

[15]  Amir Reza Shahani.  A note on the paper “Analysis of perfectly bonded wedges and bonded wedges with an interfacial crack under antiplane shear loading” , 2001 .

[16]  Rob Malouf,et al.  A Comparison of Algorithms for Maximum Entropy Parameter Estimation , 2002, CoNLL.

[17]  Lorraine K. Tanabe,et al.  Tagging gene and protein names in biomedical text , 2002, Bioinform..

[18]  K. E. Ravikumar,et al.  A Biological Named Entity Recognizer , 2002, Pacific Symposium on Biocomputing.

[19]  Jun'ichi Tsujii,et al.  GENIA corpus - a semantically annotated corpus for bio-textmining , 2003, ISMB.

[20]  Burkhard Rost,et al.  Protein names precisely peeled off free text , 2004, ISMB/ECCB.

[21]  Burr Settles,et al.  Biomedical Named Entity Recognition using Conditional Random Fields and Rich Feature Sets , 2004, NLPBA/BioNLP.

[22]  YangQuan Chen,et al.  Formation control: a review and a new consideration , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Pingjing Yao,et al.  Adaptive neural network control for a class of low-triangular-structured nonlinear systems , 2006, IEEE Transactions on Neural Networks.

[24]  C.M. Saaj,et al.  Spacecraft Swarm Navigation and Control Using Artificial Potential Field and Sliding Mode Control , 2006, 2006 IEEE International Conference on Industrial Technology.

[25]  Mark W. Schmidt,et al.  Accelerated training of conditional random fields with stochastic gradient methods , 2006, ICML.

[26]  Olga Russakovsky,et al.  Training Conditional Random Fields for Maximum Labelwise Accuracy , 2006, NIPS.

[27]  Huajing Fang,et al.  Stability analysis of stochastic swarm systems , 2006, Wuhan University Journal of Natural Sciences.

[28]  Henry A. Kautz,et al.  Training Conditional Random Fields Using Virtual Evidence Boosting , 2007, IJCAI.

[29]  Maryam Mahdaviani,et al.  Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition , 2007, NIPS.

[30]  Baris Fidan,et al.  Aggregation, Foraging, and Formation Control of Swarms with Non-Holonomic Agents Using Potential Functions and Sliding Mode Techniques ∗† , 2007 .

[31]  Veysel Gazi,et al.  Formation control with potential functions and Newton's iteration , 2007, 2007 European Control Conference (ECC).

[32]  Yang Guangyou,et al.  A Modified Particle Swarm Optimizer Algorithm , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[33]  Hans-Peter Kriegel,et al.  Extraction of semantic biomedical relations from text using conditional random fields , 2008, BMC Bioinformatics.

[34]  Long Wang,et al.  Tracking Control for Groups of Mobile Agents , 2007, 2007 American Control Conference.

[35]  W. Eric L. Grimson,et al.  Learning coupled conditional random field for image decomposition with application on object categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Lin Li,et al.  Practical Stability Analysis of Stochastic Swarms , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[37]  Monique Polit,et al.  Integration of neural networks in a geographical information system for the monitoring of a catchment area , 2008, Math. Comput. Simul..

[38]  Mustafa Türker,et al.  Dynamic Neural-Network-Based Model-Predictive Control of an Industrial Baker's Yeast Drying Process , 2008, IEEE Transactions on Neural Networks.

[39]  Marco Dorigo,et al.  Towards group transport by swarms of robots , 2009, Int. J. Bio Inspired Comput..

[40]  Stéphane Pérennes,et al.  P2P storage systems: How much locality can they tolerate? , 2009, 2009 IEEE 34th Conference on Local Computer Networks.

[41]  Jianchao Zeng,et al.  Globally Asymptotically Stable for Exponential Type Stochastic Swarms , 2009, 2009 Second International Symposium on Information Science and Engineering.

[42]  Xue-xia Zhang,et al.  Crack-tip field on mode II interface crack of double dissimilar orthotropic composite materials , 2009 .

[43]  Krishna R. Pagilla,et al.  Mathematical modeling of aerobic membrane bioreactor (MBR) using activated sludge model no. 1 (ASM1) , 2009 .

[44]  Zeng Jian-chao.  Simulation Modeling and Analysis of Dynamics of Range Limit Perceived Group , 2009 .

[45]  Jun-lin Li,et al.  Interface end stress field of antiplane of orthotropic bimaterials , 2009 .

[46]  Marcílio Carlos Pereira de Souto,et al.  On a hybrid weightless neural system , 2009, Int. J. Bio Inspired Comput..

[47]  Haihua Xu,et al.  Minimum tag error for discriminative training of conditional random fields , 2009, Inf. Sci..

[48]  M de Gracia,et al.  New generic mathematical model for WWTP sludge digesters operating under aerobic and anaerobic conditions: Model building and experimental verification. , 2009, Water research.

[49]  Steve Renals,et al.  Speech Recognition Using Augmented Conditional Random Fields , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[50]  David West,et al.  Predictive modeling for wastewater applications: Linear and nonlinear approaches , 2009, Environ. Model. Softw..

[51]  Kyle Chard,et al.  Social Cloud: Cloud Computing in Social Networks , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[52]  Jianchao Zeng,et al.  Swarm Dynamics Behavior Analysis and Coordinated Control of Limited-Range Perceived Agents , 2010 .

[53]  Xinping Guan,et al.  Target tracking and obstacle avoidance for multi-agent systems , 2010, Int. J. Autom. Comput..

[54]  Jesús Montes,et al.  Using Global Behavior Modeling to Improve QoS in Cloud Data Storage Services , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[55]  Alexander G. Loukianov,et al.  Discrete-time recurrent high order neural networks for nonlinear identification , 2010, J. Frankl. Inst..

[56]  Mohamed Meselhy Eltoukhy,et al.  The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process. , 2010, Journal of hazardous materials.

[57]  Eduardo Bayro-Corrochano,et al.  Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot , 2010, J. Frankl. Inst..

[58]  Jinnan Yang,et al.  Studies on application of cloud computing techniques in GIS , 2010, 2010 Second IITA International Conference on Geoscience and Remote Sensing.

[59]  Michael G. Epitropakis,et al.  Hardware-friendly Higher-Order Neural Network Training using Distributed Evolutionary Algorithms , 2010, Appl. Soft Comput..

[60]  Dian He,et al.  Replicate Distribution Method of Minimum Cost in Cloud Storage for Internet of Things , 2011, 2011 International Conference on Network Computing and Information Security.

[61]  Zhen Liu,et al.  Flocking Motion, Obstacle Avoidance and Formation Control of Range Limit Perceived Groups Based on Swarm Intelligence Strategy , 2011, J. Softw..

[62]  Xie Qi.  Study on the P2P Cloud Storage System , 2011 .

[63]  Qunli Zhao,et al.  Application study of online education platform based on cloud computing , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[64]  A. Kumar,et al.  Architecture for Inter-cloud Services Using IPsec VPN , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[65]  Aida Ghazizadeh,et al.  Cloud Computing Benefits and Architecture in E-Learning , 2012, 2012 IEEE Seventh International Conference on Wireless, Mobile and Ubiquitous Technology in Education.

[66]  Chien-Min Wang,et al.  Provision of Storage QoS in Distributed File Systems for Clouds , 2012, 2012 41st International Conference on Parallel Processing.