A New Learning Method for Piecewise Linear Regression
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A Clustering Technique for the Identification of Piecewise Affine Systems
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
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Many problems solved by multilayer neural networks (MLNNs) are reduced into pattern mapping. If the mapping includes several different rules, it is difficult to solve these problems by using a single MLNN with linear connection weights and continuous activation functions. In this paper, a structure trainable neural network has been proposed. The gate units are embedded, which can be trained together with the connection weights. Pattern mapping problems, which include several different, mapping rules, can be realized using a single new network. Since, some parts of the network can be commonly used for different mapping rules, the network size can be reduced compared with the modular neural networks, which consists of several independent expert networks.