Biswaranjan Panda¹, Nitin Kumar Tripathy¹, Shibashankar Sahu¹, Bikash K. Behera², Walaa E. Elhady^3,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6225-6236, 2022, DOI:10.32604/cmc.2022.028394 - 28 July 2022

Abstract In this paper, we propose a novel algorithm based on Zidan’s quantum computing model for remotely controlling the direction of a quantum-controlled mobile robot equipped with n-movements. The proposed algorithm is based on the measurement of concurrence value for the different movements of the robot. Consider a faraway robot that moves in the deep space (e.g., moves toward a galaxy), and it is required to control the direction of this robot from a ground station by some person Alice. She sends an unknown qubit α |0⟩ + β |1⟩ via the teleportation protocol to the robot. Then,… More >

Wenzheng Li¹, Yijun Gu^{1, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 755-768, 2020, DOI:10.32604/cmc.2020.07984 - 01 May 2020

Abstract As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy More >

Ching‐Yi Chen, Yi‐Jen Lin

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI:10.31209/2019.100000093

Abstract In this study, a systematic data-driven adaptive neuro-fuzzy inference system (ANFIS) modelling methodology is proposed. The new methodology employs an unsupervised competitive learning scheme to build an initial ANFIS structure from input-output data, and a high-performance PSO-LSE method is developed to improve the structure and to identify the consequent parameters of ANFIS model. This proposed modelling approach is evaluated using several nonlinear systems and is shown to outperform other modelling approaches. The experimental results demonstrate that our proposed approach is able to find the most suitable architecture with better results compared with other methods from More >