Universal learning networks with varying parameters
Kotaro Hirasawa, Jinglu Hu, J. Murata, Chun‐Zhi Jin, H. Etoh, H. Katagiri · 2003
The universal learning network (ULN) which is a superset of supervised learning networks has been already proposed. Parameters in ULN are trained in order to optimize a criterion function as conventional neural networks, and after training they are used as constant parameters. In this paper, a method to alter the parameters depending on the network flows is presented to enhance representation abilities of networks. In the proposed method, there exists two kinds of networks, the first one is a basic network which includes varying parameters and the other one is a network which calculates the optimal varying parameters depending on the network flows of the basic network. It is also proposed in this paper that any type of networks such as fuzzy inference networks, radial basis function networks and neural networks can be used for the basic and parameter calculation networks. From simulations where parameters in a neural network are altered by fuzzy inference networks, it is shown that the networks with the same number of varying parameters have higher representation abilities than the conventional networks.