Las Vegas method of region-of-attraction enlargement in neural networks
Yuri Smetanin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
The problem of the design of unerring neural networks with block structure is considered. The bottleneck is building faultless blocks without spurious states. All the commonly used rules of recurrent neural network learning don't exclude the possibility of spurious state apparition in the stable state neighborhoods. In recognition problems these spurious states cause false solutions deteriorating over the network and destroying its robustness. The problem of finding the number and localizing the stable states in neural networks with polynomial capacity without oscillations is considered. A method is proposed to detect spurious states that are close to the stable states of a given network. The method is based on random sampling. It is fast and almost always finds dangerous spurious states. The algorithm is of Las Vegas type and can find the centers and approximate sizes of all the basins of attraction that are great enough. The evaluation of the test time and the probability of the test correctness are given. The number of operations is polynomial unless the capacity is exponential. Some approaches to spurious state suppression are proposed. The results of experiments with some neural network models are briefly described.