Abstract
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.
Highlights
The developments in the field of Artificial Intelligent (AI) and Machine Learning (ML) is grown dramatically within few years ago
This contribution allows the algorithm of a gradient descent to optimize the synaptic weights. Their results demonstrated that the proposed algorithm can attain a competitive accuracy in temporal pattern classification and sound recognition. (Hu et al, 2019) proposed a probability-modulated timing mechanism which was built on the stochastic neurons, where the discontinuous spike patterns were converted to the likelihood of generating the desired output spike trains. (Wu, et al, 2019) presented an algorithm for multilayer spiking neural networks based on adaptive structure learning, in which the synaptic weights are updated according to inner product of spiking sequences and depending on the supervised learning algorithm
In the field of applying AI in system identification and control, (Han, et al, 2020) proposed a new algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems. (Genc, 2017) solved the problem of scalability and robustness issues in complex nonlinear system identification of dynamic system within the framework of deep Convolutional Neural Networks (CNNs)
Summary
The developments in the field of AI and Machine Learning (ML) is grown dramatically within few years ago. By assuming a special condition of the threshold, their algorithm simplified the equation of the membrane potential This contribution allows the algorithm of a gradient descent to optimize the synaptic weights. In the field of applying AI in system identification and control, (Han, et al, 2020) proposed a new algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems. (Genc, 2017) solved the problem of scalability and robustness issues in complex nonlinear system identification of dynamic system within the framework of deep Convolutional Neural Networks (CNNs). (Goel, et al, 2020) developed an approach for deep convolutional neural network -based patchlearning to predict the cutline by learning the network to estimate and learn the pattern around the area of the joint fingerprint.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
