With the rapid development of network information technology, distributed system coordinated control has become an indispensable part of the network information industry in the world today. In this paper, the concepts and classifications of the distributed system coordinated control method for network information mode are introduced first, then the problems existing in the traditional network structure are analyzed in detail. Then, from the point of view of genetic algorithm theory, a support vector machine (SVM), fuzzy logic and transfer function are established to solve the actual non-linear load dispatch, in which the data is processed by neural calculation. Finally, the improved decision table achieves the same control effect as the original model through experimental validation, and improves the system coordination performance index and stability. The experimental results show that the prediction time of this model is within the range of 4s-7s, and its stability, feasibility and prediction accuracy are up to 90%. This indicates that the distributed system coordination prediction model under network information mode performs well.

Mallik Alasser. Distributed System Coordination Predictive Control for Network Information Mode. Distributed Processing System (2022), Vol. 3, Issue 4: 45-52. https://doi.org/10.38007/DPS.2022.030406.

[1] Anas M. Al-Oraiqat, Oleksandr Ulichev, Yelyzaveta Meleshko, Hazim Saleh Al-Rawashdeh, Oleksii Smirnov, Liudmyla I. Polishchuk:Modeling strategies for information influence dissemination in social networks. J. Ambient Intell. Humaniz. Comput. 13(5): 2463-2477 (2022).

[2] Abdul Razaque, Syed S. Rizvi, Meer Jaro Khan, Muder Almiani, Amer Al-Rahayfeh:State-of-art review of information diffusion models and their impact on social network vulnerabilities. J. King Saud Univ. Comput. Inf. Sci. 34(1): 1275-1294 (2022).

[3] Kevin Kam Fung Yuen:Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process. Oper. Res. 22(3): 1759-1785 (2022).

[4] P. V. Sunil Kumar, Adheeba Thahsin, Manju M, Gopakumar G:A Heterogeneous Information Network Model for Long Non-Coding RNA Function Prediction. IEEE ACM Trans. Comput. Biol. Bioinform. 19(1): 255-266 (2022).

[5] Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola:GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. ACM Trans. Inf. Syst. 40(3): 45:1-45:35 (2022).

[6] Asadullah Khalid, Arif I. Sarwat:Unified Univariate-Neural Network Models for Lithium-Ion Battery State-of-Charge Forecasting Using Minimized Akaike Information Criterion Algorithm. IEEE Access 9: 39154-39170 (2021).

[7] Josué Aarón López-Leyva, Esperanza Guerra-Rosas, Josué Álvarez-Borrego:Multi-Class Diagnosis of Skin Lesions Using the Fourier Spectral Information of Images on Additive Color Model by Artificial Neural Network. IEEE Access 9: 35207-35216 (2021).

[8] Jan Treur:Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation. Cogn. Syst. Res. 68: 34-52 (2021).

[9] Rafiullah Khan, Mohib Ullah, Atif Khan, Muhammad Irfan Uddin, Maha Al-Yahya:NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols. Complex. 2021: 6651662:1-6651662:8 (2021).

[10] Narihisa Matsumoto, Yoh-ichi Mototake, Kenji Kawano, Masato Okada, Yasuko Sugase-Miyamoto:Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces. J. Comput. Neurosci. 49(3): 251-257 (2021).

[11] Sanjay Kumar, Muskan Saini, Muskan Goel, B. S. Panda:Modeling information diffusion in online social networks using a modified forest-fire model. J. Intell. Inf. Syst. 56(2): 355-377 (2021).

[12] Gasper Begus:CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks. Neural Networks 139: 305-325 (2021).

[13] Qamar Abbas, Syed Ali Hassan, Haris Pervaiz, Qiang Ni:A Markovian Model for the Analysis of Age of Information in IoT Networks. IEEE Wirel. Commun. Lett. 10(7): 1596-1600 (2021).

[14] Balázs Varga, János Farkas, Rodney Cummings, Yuanlong Jiang, Don Fedyk:Flow and Service Information Model for Deterministic Networking (DetNet). RFC 9016: 1-20 (2021).

[15] Suman Kundu, Tomasz Kajdanowicz, Przemyslaw Kazienko, Nitesh V. Chawla:Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network. IEEE Access 8: 186653-186662 (2020).

[16] Hussain K. Chaiel, Ahmed A. Alabdel Abass:Game theoretical model for information transmission in structure-free wireless sensor networks. IET Commun. 14(17): 3080-3086 (2020).

[17] María Flavia Guiñazú, Víctor Cortés, Carlos F. Ibáñez, Juan D. Velásquez:Employing online social networks in precision-medicine approach using information fusion predictive model to improve substance use surveillance: A lesson from Twitter and marijuana consumption. Inf. Fusion 55: 150-163 (2020).

[18] Samar Mouakket, Yuan Sun:Investigating the Impact of Personality Traits of Social Network Sites Users on Information Disclosure in China: the Moderating Role of Gender. Inf. Syst. Frontiers 22(6): 1305-1321 (2020).