Recognition system by neural network for additional learning
S. Shiotani, Toshio Fukuda, Takanori Shibata · 2002
The authors propose a neural network (NN) model for pattern recognition which can learn new patterns without losing patterns memorized in the past. This model is called a neural network based on distance between patterns (NDP). The NDP uses the radial basis function (RBF) as the response function in place of the sigmoid function. The response function is a smooth function similar to the Gaussian base function and the probability density function. The response function learns patterns faster than the multilayered perceptron (MLP) as well as other RBF NNs. The most salient architectural feature of the NDP is self-organization by adding nodes in the output layer one by one. The NDP also varies the curve of the response function by tuning the center and the width of the response function, and separates the input space into regions for each category appropriately.