CMAC modeling using pseudo-bacterial genetic algorithm and its acceleration

A cerebellar model arithmetic computer (CMAC) is a neural network whose advantage is fast learning. It is, however, difficult to decide on the various parameters of a CMAC in advance. The pseudo-bacterial genetic algorithm (PBGA) is an evolutionary algorithm that is efficient in local searching. This paper proposes a "PBGA/CMAC" system that decides the positions of partitions, which are the main parameters of a CMAC, using the PBGA. The PBGA/CMAC hardware is implemented for acceleration, because PBGA/CMAC needs a large amount of computation time. An efficient learning method using pipelining is also presented. It is found that the accuracy with the proposed learning method is almost the same as that of the conventional CMAC\´s learning. The PBGA/CMAC hardware is 140 times faster than that of the equivalent PBGA software