Supervised and unsupervised learning with fuzzy similarity for neural-network-based fuzzy logic control systems

A feedforward multilayered connectionist network that has distributed learning abilities is proposed to realize the basic elements and functions of a traditional fuzzy logic controller. Two complementary structure/parameter learning algorithms are proposed for setting up the proposed neural-network-based fuzzy logic control system (NN-FLCS). First, a two-phase hybrid learning algorithm is proposed which combines unsupervised and supervised learning procedures to build the rule nodes and train the membership functions. The two-phase hybrid learning algorithm performs well if sets of training data are available offline. The authors then propose an online supervised structure/parameter learning algorithm for constructing the NN-FLCS dynamically. This algorithm combines the backpropagation learning scheme for the parameter learning and a fuzzy similarity measure for the structure learning. The proposed online structure/parameter learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Computer simulation examples are presented to illustrate the performance of the learning algorithms.<
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