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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. This article is categorized under:Technologies > Computational IntelligenceTechnologies > Machine LearningApplication Areas > Science and Technology [ABSTRACT FROM AUTHOR]

This paper proposes the application of functional link neural networks (FLNNs) for structural response prediction of tall buildings due to seismic loads. The ground acceleration data are taken as input, and structural responses of different floors of multistorey shear buildings are considered as output. It is worth mentioning that handling of large earthquake data has become a great challenge in the design of tall structures viz., that of shear buildings. As such, here, a functional expansion block in FLNN has been used along with efficient Chebyshev and Legendre polynomials. Training is done with one earthquake data set, and testing is done with different intensities of other earthquake data sets; and it is seen that FLNN can very well predict the structural response of different floors of multistorey shear buildings subject to earthquake data. Results of the FLNN are compared with a multilayer neural network (MNN), and it is found that the FLNN gives better accuracy and takes less computation time compared to MNN, which shows the computational efficiency of FLNN over MNN. Numerical examples of two-, five-, and ten-storey buildings are considered, and corresponding results are presented in the form of tables and plots. [ABSTRACT FROM PUBLISHER]

Present study intends to propose identification methodologies for multistorey shear buildings using powerful technique of artificial neural network models which can handle interval data. Identification with crisp data is known and neural network method has already been used by various researchers for this case. Here, the input and output data are handled in interval form. This is because, in general, we may not get the corresponding input and output values exactly (in crisp form), but we may have only the uncertain information of the data. These uncertain data are assumed in terms of interval and the corresponding problem of system identification is investigated. [ABSTRACT FROM PUBLISHER]

This article uses powerful technique of artificial neural network (ANN) models to simulate and estimate structural response of two-storey shear building by training the model for a particular earthquake. The neural network is first trained for a real earthquake data and the numerically generated responses of different floors of two-storey buildings as the training patterns. Trained ANN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by ANN model are good for practical purposes. It is worth mentioning that although the simulation is done with numerically generated response data for particular earthquake, the idea may also be used for actual experimental (response) data. [ABSTRACT FROM AUTHOR]

The accurate long-range forecast of southwest rainfall can have manifold benefits for the country, from disaster mitigation and town planning to crop planning and power generation. In this paper, the rainfall has been modeled using artificial neural network (ANN) with different network configurations. Performance of these networks are compared with some results found in the literature. The networks have also been tested for the data outside the range of the trained data and compared with known results. The present network is found to be better in term of predictions than the previous results by others. Southwest monsoon rainfall over India for 6 years in advance has been predicted. [ABSTRACT FROM AUTHOR]