This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional-link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self-adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems.

中文翻译：

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动态系统中的机器智能：\A state-of-art review

本文致力于研究机器智能 (MI) 方法的影响，即。用于研究跨学科领域出现的动态系统的各种类型的神经模型。不同类型的人工神经网络 (ANN) 方法，即循环神经网络、功能链接神经网络、卷积神经网络、辛人工神经网络、遗传算法神经网络等，由不同的研究人员提出来研究这些问题。尽管研究人员已经开发了各种传统方法来解决这些动力学问题，但现有的传统方法有时可能会依赖于问题，需要重复模拟，并且无法解决非线性行为。在这方面，基于神经网络模型的方法更通用，解决方案在给定的集成域上是连续的，具有自适应性，可以用作黑盒。因此，在本文中，我们回顾并分析了用于调查这些问题的不同 MI 方法。