The aim of this article is to describe the formulation of the quarter-sweep iterative alternating decomposition explicit (QSIADE) method using the finite difference approach for solving one-dimensional diffusion equations. The concept of the QSIADE method is inspired via combination between the quarter-sweep iterative and the iterative alternating decomposition explicit (IADE) methods known as one of the technique in two-step iterative methods. The QSIADE method has been shown to be very fast as compared with the standard IADE method. Some numerical tests were included to support our statement.
We study a fuzzy fractional differential equation (FFDE) and
present its solution using Zadeh's extension principle. The proposed study extends the
case of fuzzy differential equations of integer order. We also propose a numerical method
to approximate the solution of FFDEs. To solve nonlinear problems, the proposed
numerical method is then incorporated into an unconstrained optimisation technique. Several
numerical examples are provided.
This paper explores the application of fuzzy differential equations in modeling of prey and predator populations. A new model, referred to as fuzzy predator-prey model is introduced. This model is then solved numerically by means of a fuzzy Euler method. Some numerical results are presented in order to show the evolution of the prey and predator populations over time. Finally, the stability of the new fuzzy model is studied and shown graphically in the fuzzy phase plane.
Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
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