1
|
Medeiros CMS, Barreto GA. A novel weight pruning method for MLP classifiers based on the MAXCORE principle. Neural Comput Appl 2011. [DOI: 10.1007/s00521-011-0748-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
2
|
Mohamed MH. Rules extraction from constructively trained neural networks based on genetic algorithms. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.04.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
|
3
|
|
4
|
Low KH, Leow WK, Ang MH. An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks. Neural Comput 2005. [DOI: 10.1162/0899766053630378] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a nonholonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated motion tasks even though the weighted-sum ensemble approach also operates in continuous motor control space.
Collapse
Affiliation(s)
- Kian Hsiang Low
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213-3890, U.S.A
| | - Wee Kheng Leow
- Department of Computer Science, National University of Singapore, Singapore 117543, Singapore
| | - Marcelo H. Ang
- Department of Mechanical Engineering, National University of Singapore, Singapore 119260, Singapore
| |
Collapse
|
5
|
|
6
|
Campbell C. Constructive learning techniques for designing neural network systems. NEURAL NETWORK SYSTEMS TECHNIQUES AND APPLICATIONS 1998. [DOI: 10.1016/s1874-5946(98)80005-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
7
|
Littmann E, Ritter H. Adaptive color segmentation-a comparison of neural and statistical methods. ACTA ACUST UNITED AC 1997; 8:175-85. [DOI: 10.1109/72.554203] [Citation(s) in RCA: 90] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|