This paper is centered on designing an adaptive optimal control framework for regulating the temperature in a catalytic flow reversal reactor (CFRR), employing an integral reinforcement learning (IRL) technique. Initi...
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The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the pr...
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The purpose of this paper is to present a numerical approach based on the artificial neural networks(ANNs)for solving a novel fractional chaotic financial model that represents the effect of memory and chaos in the presented *** method is constructed with the combination of the ANNs along with the Levenberg-Marquardt backpropagation(LMB),named the *** technique is tested for solving the novel problem for three cases of the fractional-order values and the obtained results are compared with the reference *** numbers neurons have been used to solve the fractional-order chaotic financial *** selection of the data to solve the fractional-order chaotic financial model are selected as 75%for training,10%for testing,and 15%for *** results indicate that the presented approximate solutions fit exactly with the reference solution and the method is effective and *** obtained results are testified to reduce the mean square error(MSE)for solving the fractional model and verified through the various measures including correlation,MSE,regression histogram of the errors,and state transition(ST).
Stroke is one of the leading causes of disability and death worldwide, underscoring the need for early and precise diagnosis to enhance patient outcomes. Artificial intelligence (AI) has emerged as a promising tool fo...
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In this paper, we present the fractional-order chaotic complex permanent magnet synchronous motor system (FOCPMSM). The dynamical behavior of the FOCPMSM system such as phase portraits, bifurcation diagrams, Lyapunov ...
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This paper develops neural network ensembles to perform solid rocket motor classification based on trajectory data. Two classes of solid rocket motors are defined and fly-out data was generated using a 6-DOF code. Thr...
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In this paper, by constructing a coupling equation, we establish the Harnack type inequalities for stochastic differential equations driven by fractional Brownian motion with Markovian switching. The Hurst parameter H...
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In this paper, by constructing a coupling equation, we establish the Harnack type inequalities for stochastic differential equations driven by fractional Brownian motion with Markovian switching. The Hurst parameter H is supposed to be in(1/2, 1). As a direct application, the strong Feller property is presented.
We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured,...
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In this article, the concept of fuzzy (m, n)-quasi-ideals in ordered semigroups is developed and discussed in various ways. In addition, we present the concepts of fuzzy (m, 0)-ideals and fuzzy (0, n)-i...
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Machine learning algorithms can improve the time series data analysis as compared to the traditional methods such as moving averages or auto-regressive approaches. This advancement has helped to unlock several challen...
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This paper focuses on the optimization of overparameterized, non-convex low-rank matrix sensing (LRMS)-an essential component in contemporary statistics and machine learning. Recent years have witnessed significant br...
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