Calculus - For The Practical Man : Thompson, J. E. : Free Download ...[^3^]

First published in 1945, this edition of J. E. Thompson's Calculus for the Practical Man is the ideal simple guide for those who are studying physics or mathematical courses at university, or for those who wish to brush up on the calculus they learnt while in higher education. Each chapter features illustrated examples of solved problems, and there are practice exercises for the reader to try at the end of each section.

In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional calculus (FC) theory has been developed to summarize the main features and applications of the ANNs. ANN is a mathematical modeling tool used in several sciences and engineering fields. FC has been mainly applied on ANNs with three different objectives, such as systems stabilization, systems synchronization, and parameters training, using optimization algorithms. FC and some control strategies have been satisfactorily employed to attain the synchronization and stabilization of ANNs. To show this fact, in this manuscript are summarized, the architecture of the systems, the control strategies, and the fractional derivatives used in each research work, also, the achieved goals are presented. Regarding the parameters training using optimization algorithms issue, in this manuscript, the systems types, the fractional derivatives involved, and the optimization algorithm employed to train the ANN parameters are also presented. In most of the works found in the literature where ANNs and FC are involved, the authors focused on controlling the systems using synchronization and stabilization. Furthermore, recent applications of ANNs with FC in several fields such as medicine, cryptographic, image processing, robotic are reviewed in detail in this manuscript. Works with applications, such as chaos analysis, functions approximation, heat transfer process, periodicity, and dissipativity, also were included. Almost to the end of the paper, several future research topics arising on ANNs involved with FC are recommended to the researchers community. From the bibliographic review, we concluded that the Caputo derivative is the most utilized derivative for solving problems with ANNs because its initial values take the same form as the differential equations of integer-order.

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Artificial neural networks (ANNs) have emerged as a promising alternative to simulate systems due to their successful applications in several engineering and science fields, such as signal processing, image processing, control systems, associative memory, to name a few. Besides, fractional calculus (FC) is an extension and generalization of the integer-order calculus, which its main characteristic is the memory description. When the ANNs are modeled using fractional differential equations (FDE), they are named fractional artificial neural networks (FANNs). The FDE is used for describing the dynamical behavior of the ANNs neurons. Hence, in the last decade, many authors employed FANN for modeling physics and engineering systems more efficiently and accurately. 2ff7e9595c