Deep belief network and linear perceptron based cognitive computing for collaborative robots

Abstract

Objective: This paper is to analyze the performance of the control system of collaborative robots based on cognitive computing technology. Methods: This study combines cognitive computing and deep belief network algorithms with collaborative robots to construct a cognitive computing system model based on deep belief networks, which is applied to the control system of collaborative robots. Further, the simulation is used to compare and analyze the algorithm performance of deep belief network (DBN), multilayer perceptron (MLP) and the cognitive computing system model of deep belief network and linear perceptron (DBNLP) proposed in this study. Results: The results show that compared with the DBN and MLP algorithms, the DBNLP algorithm model has a significantly lower error rate in the number of repetitions of the training set, the number of hidden neurons, and the number of network layers. And the number of task backlog, the number of resources to be allocated and the time consumption are less, as well as the accuracy is high. After comparing and analyzing the changes in the estimated value of Ex (expected value), En (entropy value) and He (hyper entropy value), it is found that the estimated value of the DBNLP algorithm model is closer to the true value than that of the DBN and MLP algorithms. Conclusion: The application of the DBNLP algorithm model to collaborative robots can significantly improve its accuracy and safety, providing an experimental basis for the performance improvement of later collaborative robots.