## Advances in Neural Networks - ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 ProceedingsThis book and its companion volumes, LNCS vols. 5551, 5552 and 5553, constitute the proceedings of the 6th International Symposium on Neural Networks (ISNN 2009), held during May 26–29, 2009 in Wuhan, China. Over the past few years, ISNN has matured into a well-established premier international symposium on neural n- works and related fields, with a successful sequence of ISNN symposia held in Dalian (2004), Chongqing (2005), Chengdu (2006), Nanjing (2007), and Beijing (2008). Following the tradition of the ISNN series, ISNN 2009 provided a high-level inter- tional forum for scientists, engineers, and educators to present state-of-the-art research in neural networks and related fields, and also to discuss with international colleagues on the major opportunities and challenges for future neural network research. Over the past decades, the neural network community has witnessed tremendous - forts and developments in all aspects of neural network research, including theoretical foundations, architectures and network organizations, modeling and simulation, - pirical study, as well as a wide range of applications across different domains. The recent developments of science and technology, including neuroscience, computer science, cognitive science, nano-technologies and engineering design, among others, have provided significant new understandings and technological solutions to move the neural network research toward the development of complex, large-scale, and n- worked brain-like intelligent systems. This long-term goal can only be achieved with the continuous efforts of the community to seriously investigate different issues of the neural networks and related fields. |

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### Contents

I | 1 |

II | 11 |

III | 21 |

IV | 29 |

V | 36 |

VI | 46 |

VII | 52 |

VIII | 60 |

LXX | 617 |

LXXI | 624 |

LXXII | 635 |

LXXIII | 643 |

LXXIV | 651 |

LXXV | 661 |

LXXVI | 670 |

LXXVII | 679 |

IX | 68 |

X | 75 |

XI | 84 |

XII | 94 |

XIII | 104 |

XIV | 110 |

XV | 118 |

XVI | 128 |

XVII | 138 |

XVIII | 149 |

XIX | 156 |

XX | 164 |

XXI | 175 |

XXII | 185 |

XXIII | 194 |

XXIV | 201 |

XXV | 209 |

XXVI | 219 |

XXVII | 229 |

XXVIII | 238 |

XXIX | 244 |

XXX | 253 |

XXXI | 262 |

XXXII | 272 |

XXXIII | 279 |

XXXIV | 286 |

XXXV | 295 |

XXXVI | 303 |

XXXVII | 313 |

XXXVIII | 323 |

XXXIX | 333 |

XL | 340 |

XLI | 347 |

XLII | 357 |

XLIII | 366 |

XLIV | 375 |

XLV | 383 |

XLVI | 395 |

XLVII | 405 |

XLVIII | 413 |

XLIX | 423 |

L | 433 |

LI | 440 |

LII | 450 |

LIII | 455 |

LIV | 463 |

LV | 472 |

LVI | 482 |

LVII | 492 |

LVIII | 503 |

LIX | 512 |

LX | 522 |

LXI | 532 |

LXII | 542 |

LXIII | 550 |

LXIV | 560 |

LXV | 570 |

LXVI | 579 |

LXVII | 589 |

LXVIII | 601 |

LXIX | 607 |

LXXVIII | 689 |

LXXIX | 699 |

LXXX | 707 |

LXXXI | 717 |

LXXXII | 728 |

LXXXIII | 735 |

LXXXIV | 745 |

LXXXV | 756 |

LXXXVI | 766 |

LXXXVII | 774 |

LXXXVIII | 784 |

LXXXIX | 794 |

XC | 804 |

XCI | 813 |

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XCIII | 827 |

XCIV | 836 |

XCV | 844 |

XCVI | 852 |

XCVII | 863 |

XCVIII | 870 |

XCIX | 877 |

C | 887 |

CI | 893 |

CII | 901 |

CIII | 909 |

CIV | 919 |

CV | 929 |

CVI | 937 |

CVII | 946 |

CVIII | 956 |

CIX | 967 |

CX | 976 |

CXI | 986 |

CXII | 993 |

CXIII | 1002 |

CXIV | 1014 |

CXV | 1024 |

CXVI | 1033 |

CXVII | 1041 |

CXVIII | 1053 |

CXIX | 1062 |

CXX | 1072 |

CXXI | 1080 |

CXXII | 1090 |

CXXIII | 1098 |

CXXIV | 1107 |

CXXV | 1115 |

CXXVI | 1123 |

CXXVII | 1131 |

CXXVIII | 1138 |

CXXIX | 1144 |

CXXX | 1154 |

CXXXI | 1161 |

CXXXII | 1171 |

CXXXIII | 1181 |

CXXXIV | 1191 |

CXXXV | 1202 |

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### Other editions - View all

Advances in Neural Networks - ISNN 2009: 6th International Symposium ..., Part 1 Wen Yu,Haibo He,Nian Zhang No preview available - 2009 |

### Common terms and phrases

activation functions applications Artificial Neural Networks asymptotic stability basis function Berlin Heidelberg 2009 BP neural network Cellular Neural Networks Chen China complex Computer constant convergence data set Delayed Neural Networks denotes diﬀerent Diﬀerential Distributed Delays dynamic equation equilibrium point error estimation exist feedback ﬁrst Fractals Global Asymptotic Global Exponential Stability hidden layer Hopﬁeld IEEE IEEE Trans IEEE Transactions inequality input integration ISNN Keywords label learning algorithm Lemma linear linear matrix inequality LNCS Lyapunov function M-matrix Machine Learning matrix method networks with time-varying neuron nodes nonlinear obtained optimal output paper parameters patterns performance prediction problem proposed recurrent neural networks samples Science semi-supervised learning sensor signal simulation solution space Springer-Verlag Berlin Heidelberg stochastic structure synchronization Theorem theory time-varying delays tion variable vector Wang wavelet weights Zhang Eds