Growing algorithm of laguerre orthogonal basis neural network with weights directly determined

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5227 LNAI, Issue , 2008, Pages 60-67

  Zhang, Yunong ^a   Zhong, Tongke ^a   Li, Wei ^a   Xiao, Xiuchun ^a,b   Yi, Chenfu ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b GUANGDONG OCEAN UNIVERSITY   (China)

Author keywords

Growing algorithm; Laguerre orthogonal polynomials; Neural networks; Weights direct determination

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION ALGORITHMS; ELECTRIC NETWORK ANALYSIS; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; VEGETATION;

BP NEURAL NETWORKS; GROWING ALGORITHM; INTELLIGENT COMPUTING; INTERNATIONAL CONFERENCES; LAGUERRE ORTHOGONAL POLYNOMIALS; ONE STEP; OPTIMAL WEIGHTING; ORTHOGONAL BASIS; SIMULATION RESULTS; SPECIAL STRUCTURE; THEORETICAL ANALYSIS; WEIGHTS-DIRECT- DETERMINATION;

NEURAL NETWORKS;

EID: 53049105106     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85984-0_8     Document Type: Conference Paper

References (13)

1. Zhang, Y., Wang, J.: Recurrent Neural Networks for Nonlinear Output Regulation. Automatica 37(8), 1161-1173 (2001)
2. Zhang, Y., Ge, S.S., Lee, T.H.: A Unified Quadratic-Programming-Based Dynamical System Approach to Joint Torque Optimization of Physically Constrained Redundant Manipulators. IEEE Transactions on Systems, Man, and Cybernetics 34(5), 2126-2132 (2004)
3. Zhang, Y., Jiang, D., Wang, J.: A Recurrent Neural Network for Solving Sylvester Equation with Time-Varying Coefficients. IEEE Transactions on Neural Networks 13(5), 1053-1063 (2002)
4. Zhang, Y., Wang, J.: Global Exponential Stability of Recurrent Neural Networks for Synthesizing Linear Feedback Control Systems via Pole Assignment. IEEE Transactions on Neural Networks 13(3), 633-644 (2002)
5. Zhang, Y., Ge, S.S.: Design and Analysis of a General Recurrent Neural Network Model for Time-Varying Matrix Inversion. IEEE Transactions on Neural Networks 16(6), 1477-1490 (2005)
6. Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine 4(2), 4-22 (1987)
7. Carl, G.L.: Advances in Feedforward Neural Networks: Demystifying Knowledge Acquiring Black Boxes. IEEE Transactions on Knowledge and Data Engineering 8(2), 211-226 (1996)
8. Daniel, S.Y., Zeng, X.Q.: Hidden Neuron Pruning for Multilayer Perceptions Using a Sensitivity Measure. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, pp. 1751-1757. IEEE Press, New York (2002)
9. Niu, B., Zhu, Y.L., Hu, K.Y., Li, S.F., He, X.X.: A Cooperative Evolutionary System for Designing Neural Networks. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 12-21. Springer, Heidelberg (2006)
10. Wilson, D.R., Martinez, T.R.: The Need for Small Learning Rates on Large Problems. In: Proceedings of International Joint Conference on Neural Networks, vol. 1, pp. 115-119. IEEE Press, New York (2001)
11. Yu, X.H., Chen, G.A.: On the Local Minima Free Condition of Backpropagation Learning. IEEE Transactions on Neural Networks 6(5), 1300-1303 (1995)
12. Lin, CS.: Numerical Analysis (in Chinese). Science Press, Beijing (2006)
13. Kincaid, D., Cheney, W.: Numerical Analysis: Mathematics of Scientific Computing. China Machine Press, Beijing (2003)