Increasing the efficiency of a neural network through unlearning
✍ Scribed by J.L. Van Hemmen; L.B. Ioffe; R. Kühn; M. Vaas
- Publisher
- Elsevier Science
- Year
- 1990
- Tongue
- English
- Weight
- 371 KB
- Volume
- 163
- Category
- Article
- ISSN
- 0378-4371
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an arc
This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a repre
The ability of a neural network with one hidden layer to accurately learn a specified learning set increases with the number of nodes in the hidden layer; if a network has exactly the same number of internal nodes as the number of examples to be learnt, it is theoretically able to learn these exampl
A neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems. The approach relies on the use of vibration measurements from a 'healthy' system to train a neural network for identification purposes. Subsequently, the trained network is