On the basis of CMAC-PDM (Pattern Discrimination Model) , a novel algorithmof CMAC for monitoring machine degradation is proposed in this paper. The output of CMAC with thenovel algorithm represents the state of a mac...On the basis of CMAC-PDM (Pattern Discrimination Model) , a novel algorithmof CMAC for monitoring machine degradation is proposed in this paper. The output of CMAC with thenovel algorithm represents the state of a machine and PDM is not needed. The principle was explainedby analyzing the modified mapping process of CMAC. The novel CMAC is applied to a tool conditionmonitoring system and two methodologies (novel CMAC and CMAC-PDM) are compared. The results provethat the novel algorithm is feasible and its computational complexity is reduced significantly.展开更多
Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be em- bedded in ma...Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be em- bedded in machine for real-time decision. This paper presents a credit-assignment cerebellar model articulation controller (CA-CMAC) algorithm to reduce learning interference in machine learning. The developed algorithms on credit matrix and the credit correlation matrix are presented. The error of the training sample distributed to the activated memory cell is proportional to the cell’s credibility, which is determined by its activated times. The convergence processes of CA-CMAC in cyclic learning are further analyzed with two convergence theorems. In addition, simulation results on the inverse kinematics of 2- degree-of-freedom planar robot arm are used to prove the convergence theorems and show that CA-CMAC converges faster than conventional machine learning.展开更多
文摘On the basis of CMAC-PDM (Pattern Discrimination Model) , a novel algorithmof CMAC for monitoring machine degradation is proposed in this paper. The output of CMAC with thenovel algorithm represents the state of a machine and PDM is not needed. The principle was explainedby analyzing the modified mapping process of CMAC. The novel CMAC is applied to a tool conditionmonitoring system and two methodologies (novel CMAC and CMAC-PDM) are compared. The results provethat the novel algorithm is feasible and its computational complexity is reduced significantly.
基金Supported by the National Natural Science Foundation of China ( No. 50128504)
文摘Smart machine necessitates self-learning capabilities to assess its own performance and predict its behavior. To achieve self-maintenance intelligence, robust and fast learning algorithms need to be em- bedded in machine for real-time decision. This paper presents a credit-assignment cerebellar model articulation controller (CA-CMAC) algorithm to reduce learning interference in machine learning. The developed algorithms on credit matrix and the credit correlation matrix are presented. The error of the training sample distributed to the activated memory cell is proportional to the cell’s credibility, which is determined by its activated times. The convergence processes of CA-CMAC in cyclic learning are further analyzed with two convergence theorems. In addition, simulation results on the inverse kinematics of 2- degree-of-freedom planar robot arm are used to prove the convergence theorems and show that CA-CMAC converges faster than conventional machine learning.
