MFNNs（Monolithic Fuzzy Neural Networks）proposed in this paper combine the advantages of Fuzzy Logic Control and Aritificial Neural Network Control

not only behave well in object control but also have abilities of model self-learning and on-line self-adjustment. A series of self-learning algorithms for MENNs can be obtained based on ANNs theory

Fuzzy Set theory and its special structure. Some simulation results based on MFNNs gradient algorithm show that the convergence performance of MFNNs is much better than ordinary ANNs

the self-learning and self-adjustment are practicable

simple and effective. The learning model can be used for intelligent control system including the complicated object control. The VLSI realization of the learning MFNNs can be implemented easily.