Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
On stabilization of gradient-based training strategies for computationally intelligent systems
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
Compact representation of knowledge having strong internal interactions has become possible with the developments in neurocomputing and neural information processing. The field of neural networks has offered various solutions for complex problems, however, the problems associated with the learning performance has constituted a major drawback in terms of the realization performance and computational requirements. This paper discusses the use of variable structure systems theory in learning process. The objective is to incorporate the robustness of the approach into the training dynamics, and to ensure the stability in the adjustable parameter space. The results discussed demonstrate the fulfillment of the design specifications and display how the strength of a robust control scheme could be an integral part of a learning system. This paper discusses how Gaussian radial basis function neural networks could be utilized to drive a mechatronic system’s behavior into a predefined sliding regime, and it is seen that the results are promising.