The use of problem knowledge to improve the robustness of a fuzzy neural network

Neural networks generally take a long time to train. This is because the network is initialized using random values for the weights. These random values have no relationship to the problem to be solved. The network is also more likely to converge to a non-optimal solution when initialized with random weights. This paper discusses how a fuzzy neural network can be initialized using problem knowledge. This initialization method improves the network robustness when training using uncertain data. It is shown that the use of problem knowledge-based rules can compensate for the uncertainty in the training data