Abstract
As an incremental learning model, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only a new training sample but also some memory items stored in Long-Term Memory are trained based on a gradient descent method. The gradient descent method is generally slow and tends to be fallen into local minima. To improve these problems, we propose a fast incremental learning of RAN-LTM based on the linear method. In this algorithm, centers of basis functions are not trained but selected based on the output errors. A distinctive feature of the proposed model is that this model dose not need so much memory capacity. To evaluate the performance of our proposed model, we apply it to some function approximation problems. From the experimental results, it is verified that the proposed model can learn fast and accurately unless incremental learning is conducted over a long period of time.
