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Cerebellar Model Articulation Controller (CMAC) NN is a computational model of cerebellum introduced as an alternative to backpropagated multilayer networks to control robot arms. From then it has seen many improvements and has been applied in many other areas as a general NN. These improvements have been in the context of generalization, learning techniques, differentiability, memory size, fuzzification...
Cerebellum model articulation controller (CMAC) is known as a feedforward neural network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that...
CMAC computational model that is based on the cerebellum structure is known as a Neural Network with high computation and learning speed. Fuzzy CMAC, by introducing fuzzy reasoning to CMAC, converts it from a black box to a white box whose performance can be interpreted using fuzzy rules. Fuzzy CMAC compared to CMAC has higher approximation and modeling capability and allows for model gradient computation...
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