The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed structure has the ability to predict the response of dynamical systems more powerful than with the CNN. The proposed structure is more powerful than the CNN by 28.33% in terms of minimizing the root mean square error.
Receipt date:6/3/2021 acceptance date:4/5/2021 Publication date:31/31/2021
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The research in the role of variables contact for non-state actors have become more influential in the current of contemporary events, that related with the reality of seeking services and providing all of that in favor of maintaining the social peace, and ensuring its empowerment in order to make peace and stability outcomes as a real fa
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