9 Conclusion
In this chapter, a general overview of artificial neural networks has been presented. These networks vary in their sophistication from the very simple to the more complex. As a result, their training techniques vary as well as their capabilities and suitability for certain applications. Neural networks have attracted a lot of interest over the last few decades, and it is expected they will be an active area of research for years to come. Undoubtedly, more robust neural techniques will be introduced in the future that could benefit a wide range of complex applications.
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Keywords
- Neural Network
- Artificial Neural Network
- Hide Layer
- Radial Basis Function
- Radial Basis Function Neural Network
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References
S. I. Gallant (1993): Neural Network Learning and Expert Systems, MIT Press.
N.B. Karayiannis and A.N. Venetsanopoulos, (1993) Efficient learning algorithms for neural networks (ELEANNE), IEEE Transactions on Systems, Man and Cybernetics, 23(5), 1372–1383.
M.H. Hassoun and D.W. Clark (1988): An adaptive attentive learning algorithm for single-layer neural networks, in Proceedings of the IEEE International Conference on Neural Networks, 1, 431–440.
M.E. Ulug (1994): A single layer fast learning fuzzy controller/filter: Neural Networks, in Proceedings of the IEEE World Congress on Computational Intelligence, 3, 1662–1667.
N.B. Karayiannis and A.N. Venetsanopoulos (1992): Fast learning algorithms for neural networks, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 39(7), 453–474.
T. Hrycej (1991): Back to single-layer learning principles, in Proceedings of the International Joint Conference on Neural Networks, Seattle, 2, 945.
M.J. Healy (1991): A logical architecture for supervised learning: Neural Networks, in Proceedings of the IEEE International Joint Conference on Neural Networks, 1, 190–195.
R.D. Brandt and L. Feng (1996): Supervised learning in neural networks without feedback network, in Proceedings of the IEEE International Symposium on Intelligent Control, pp. 86–90.
Y. Gong and P. Yan (1995): Neural network based iterative learning controller for robot manipulators, in Proceedings of the IEEE International Conference on Robotics and Automation, 1, 569–574.
S. Park and T. Han (2000): Iterative inversion of fuzzified neural networks, IEEE Transactions on Fuzzy Systems, 8(3), 266–280.
X. Zhan, K. Zhao, S. Wu, M. Wang, and H. Hu (1997): Iterative learning control for nonlinear systems based on neural networks, in Proceedings of the IEEE International Conference on Intelligent Processing Systems, 1, 517–520.
C.J. Chen, A.L. Haque, and J.Y. Cheung (1992): An efficient simulation model of the Hopfield neural networks, in Proceedings of the International Joint Conference on Neural Networks, 1, 471–475.
G. Galan-Marin and J. Munoz-Perez (2001): Design and analysis of maximum Hopfield networks, IEEE Transactions on Neural Networks, 12(2), 329–339.
N.M. Nasrabadi and W. Li (1991): Object recognition by a Hopfield neural network, IEEE Transactions on Systems, Man and Cybernetics, 21(6), 1523–1535.
J. Xu, X. Zhang, and Y. Li (2001): Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR, in Proceedings of the International Joint Conference on Neural Networks, 2, 1486–1491.
T. Hayasaka, N. Toda, S. Usui, and K. Hagiwara (1996): On the least square error and prediction square error of function representation with discrete variable basis, in Proceedings of the Workshop on Neural Networks for Signal Processing, 6, 72–81. IEEE Signal Processing Society.
D.-C. Park (2000): Centroid neural network for unsupervised competitive learning, IEEE Transactions on Neural Networks, 11(2), 520–528.
W. Pedrycz and J. Waletzky (1997): Neural-network front ends in unsupervised learning, IEEE Transactions on Neural Networks, 8(2), 390–401.
D.-C. Park (1997): Development of a neural network algorithm for unsupervised competitive learning, in Proceedings of the International Conference on Neural Networks, 3, 1989–1993.
K.-R. Hsieh and W.-T. Chen (1993): A neural network model which combines unsupervised and supervised learning, IEEE Transactions on Neural Networks, 4(2), 357–360.
A.L. Dajani, M. Kamel, and M.I. Elmastry (1990): Single layer potential function neural network for unsupervised learning, in Proceedings of the International Joint Conference on Neural Networks, 2, 273–278.
M. Georgiopoulos, G.L. Heileman, and J. Huang (1991): Properties of learning in ART1, in Proceedings of the IEEE International Joint Conference on Neural Networks, 3, 2671–2676.
G.L. Heileman, M. Georgiopoulos, and J. Hwang (1994): A survey of learning results for ART1 networks, in the Proceedings of the IEEE International Conference on Neural Networks, IEEE World Congress on Computational Intelligence, 2, 1222–1225.
J. Song and M.H. Hassoun (1990): Learning with hidden targets, in the Proceedings of the International Joint Conference on Neural Networks, 3, 93–98.
H.K. Kwan (1991): Multilayer feedbackward neural networks, in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 2, 1145–1148.
J.F. Shepanski (1988): Fast learning in artificial neural systems: multilayer perceptron training using optimal estimation, in Proceedings of the IEEE International Conference on Neural Networks, 1, 465–472.
N.B. Karayiannis and M.M. Randolph-Gips (2003): On the construction and training of reformulated radial basis function neural networks, IEEE Transactions on Neural Networks, 14(4), 835–846.
J.A. Leonard and M.A. Kramer (1991): Radial basis function networks for classifying process faults, IEEE Control Systems Magazine, 11(3), 31–38.
R. Li, G. Lebby, and S. Baghavan (2002): Performance evaluation of Gaussian radial basis function network classifiers, SoutheastCon, 2002, Proceedings IEEE, pp. 355–358.
F. Heimes and B. van Heuveln (1998): The normalized radial basis function neural network, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2, 1609–1614.
R.J. Craddock and K. Warwick (1996): Multi-layer radial basis function networks. An extension to the radial basis function, in the Proceedings of the IEEE International Conference on Neural Networks, 2, 700–705.
J.C. Carr, W.R. Fright and R.K. Beatson (1997): Surface interpolation with radial basis functions for medical imaging, IEEE Transactions on Medical Imaging, 16(1), 96–107.
M.A. Romyaldy Jr. (2000): Observations and guidelines on interpolation with radial basis function network for one dimensional approximation problem, in the Proceedings of the 26th Annual Conference of the IEEE Industrial Electronics Society, 3, 2129–2134.
H. Leung, T. Lo, and S. Wang, (2001): Prediction of noisy chaotic time series using an optimal radial basis function neural network, IEEE Transactions on Neural Networks, 12(5), 1163–1172.
R. Katayama, Y. Kajitani, K. Kuwata, and Y. Nishida (1993): Self generating radial basis function as neuro-fuzzy model and its application to nonlinear prediction of chaotic time series, in a Proceedings of the Second IEEE International Conference on Fuzzy Systems, pp. 407–414.
K. Warwick and R. Craddock (1996): An introduction to radial basis functions for system identification. A comparison with other neural network methods, in the Proceedings of the 35th IEEE Decision and Control Conference, 1, 464–469.
Y. Lu, N. Sundararajan and P. Saratchandran (1996): Adaptive nonlinear system identification using minimal radial basis function neural networks, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 6, 3521–3524.
S. Tan, J. Hao, and J. Vandewalle (1995): A new learning algorithm for RBF neural networks with applications to nonlinear system identification, in Proceedings of the IEEE International Symposium on Circuits and Systems, 3, 1708–1711.
T. Ibayashi, T. Hoya, and Y. Ishida (2002): A model-following adaptive controller using radial basis function networks, in Proceedings of the International Conference on Control Applications, 2, 820–824.
P.K. Dash, S. Mishra and G. Panda (2000): A radial basis function neural network controller for UPFC, IEEE Transactions on Power Systems, 15(4), 1293–1299.
J. Deng, S. Narasimhan, and P. Saratchandran (2002): Communication channel equalization using complex-valued minimal radial basis function neural networks, IEEE Transactions on Neural Networks, 13(3), 687–696.
J. Lee, C.D. Beach, and N. Tepedelenlioglu (1996): Channel equalization using radial basis function network, in Proceedings of the IEEE International Conference on Neural Networks, 4, 1924–1928.
J. Lee, C.D. Beach, and N. Tepedelenlioglu (1996): Channel equalization using radial basis function network, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, 1719–1722.
R. Sankar and N.S. Sethi (1997): Robust speech recognition techniques using a radial basis function neural network for mobile applications, in Proceedings of IEEE Southeastcon, pp. 87–91.
H. Ney (1991): Speech recognition in a neural network framework: discriminative training of Gaussian models and mixture densities as radial basis functions, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, 573–576.
I. Cha and S.A. Kassam (1994): Nonlinear image restoration by radial basis function networks, in Proceedings of the IEEE International Conference on Image Processing, 2, 580–584.
I. Cha and S.A. Kassam (1996): Nonlinear color image restoration using extended radial basis function networks, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 6, 3402–3405.
A.G. Bors and I. Pitas (1998): Optical flow estimation and moving object segmentation based on median radial basis function network, IEEE Transactions on Image Processing, 7(5), 693–702.
D. Gao and G. Yang (2002): Adaptive RBF neural networks for pattern classifications, in Proceedings of the International Joint Conference on Neural Networks, 1, 846–851.
C. Fan, Z. Jin, J. Zhang, and W. Tian (2002): Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS, in Proceedings of the 7th International Conference on Control, Automation, Robotics and Vision, 3, 1557–1562.
J.T. Tou and R.C. Gonzalez (1974), Pattern Recognition, Reading, MA, Addison-Wesley.
Z.-P. Lo, Y. Yu, and B. Bavarian (1992): Derivation of learning vector quantization algorithms, in Proceedings of the International Joint Conference on Neural Networks, 3, 561–566.
P. Burrascano (1991): Learning vector quantization for the probabilistic neural network, IEEE Transactions on Neural Networks, 2(4), 458–461.
N.B. Karayiannis and M.M. Randolph-Gips (2003): Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: multinorm algorithms, IEEE Transactions on Neural Networks, 14(1), 89–102.
L. Medsker (1994): Design and development of hybrid neural network and expert systems, in Proceedings of the IEEE International Conference on Neural Networks, IEEE World Congress on Computational Intelligence, 3, 1470–1474.
M.S. Kurzyn (1993): Expert systems and neural networks: a comparison, Artificial Neural Networks and Expert Systems, in Proceedings of the First International Two-Stream Conference on Neural Networks, New Zealand, pp. 222–223.
A.V. Hudli, M.J. Palakal and M.J. Zoran (1991): A neural network based expert system model, in Proceedings of the Third International Conference on Tools for Artificial Intelligence, pp. 145–149.
W.-Y. Wang, C.-Y. Cheng and Y.-G. Leu (2004): An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems, in IEEE Transactions on Systems, Man and Cybernetics, Part B, 34(1), 334–345.
Y. Zhang, P.-Y. Peng and Z.-P. Jiang (2000): Stable neural controller design for unknown nonlinear systems using backstepping, IEEE Transactions on Neural Networks, 11(6), 1347–1360.
A.L. Nelson, E. Grant and G. Lee (2003): Developing evolutionary neural controllers for teams of mobile robots playing a complex game, in Proceedings of the IEEE International Conference on Information Reuse and Integration, pp. 212–218.
L. Rothrock (1992): Modeling human perceptual decision-making using an artificial neural network, in Proceedings of the International Joint Conference on Neural Networks, 2, 448–452.
S. Mukhopadhyay and H. Wang (1999): Distributed decomposition architectures for neural decision-makers, in Proceedings of the 38th IEEE Conference on Decision and Control, 3, 2635–2640.
G. Rogova, P. Scott, and C. Lolett (2002): Distributed reinforcement learning for sequential decision making, in Proceedings of the Fifth International Conference on Information Fusion, 2, 1263–1268.
J. Taheri and N. Sadati, (2003): Fully modular online controller for robot navigation in static and dynamic environments, in Proceedings of the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, 1, 163–168.
N. Sadati and J. Taheri (2002): Genetic algorithm in robot path planning problem in crisp and fuzzified environments, in Proceedings of the IEEE International Conference on Industrial Technology, 1, 175–180.
N. Sadati and J. Taheri (2002): Solving robot motion planning problem using Hopfield neural network in a fuzzified environment, in Proceedings of IEEE International Conference on Fuzzy Systems, 2, 1144–1149.
R. Bambang (2002): Active noise cancellation using recurrent radial basis function neural networks, in Proceedings of the Asia-Pacific Conference on Circuits and Systems, 2, 231–236.
C.K. Chen and T.-D. Chiueh (1996): Multilayer perceptron neural networks for active noise cancellation, in Proceedings of the IEEE International Symposium on Circuits and Systems, 3, 523–526.
L. Tao and H.K. Kwan (1999): A neural network method for adaptive noise cancellation, circuits and systems, in Proceedings of the IEEE International Symposium on Circuits and Systems, 5, 567–570.
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Taheri, J., Zomaya, A.Y. (2006). Artificial Neural Networks. In: Zomaya, A.Y. (eds) Handbook of Nature-Inspired and Innovative Computing. Springer, Boston, MA. https://doi.org/10.1007/0-387-27705-6_5
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