Програмна система для дослідження паралельних алгоритмів з використанням обчислень на графічному процесорі

Розроблено програмне забезпечення для дослідження паралельних алгоритмів сегментації зображень з використанням обчислень на графічному процесоріThe software for the study of parallel algorithms for image segmentation using computation on GPUs is developed and presente

Инструментальные средства построения моделей нелинейных систем в виде рядов Вольтерра в частотной области

Предлагается программно-аппаратный комплекс идентификации нелинейных динамических объектов на основе моделей Вольтерра в частотной области с использованием полигармонических тестовых сигналов. Применение метода и инструментальных средств демонстрируется на примере построения модели канала связиThe hardware and software toolkit is presented. It can effectively implement computer simulation of nonlinear dynamic objects with the structure of the "black box" with the mathematical apparatus of Volterra series and obtain the frequency response of these objects. Using of the method and tools are implemented on receiving of communication channel mode

Програмна система для дослідження паралельних алгоритмів з використанням обчислень на графічному процесорі

Розроблено програмне забезпечення для дослідження паралельних алгоритмів сегментації зображень з використанням обчислень на графічному процесоріThe software for the study of parallel algorithms for image segmentation using computation on GPUs is developed and presente

Применение вейвлет–фильтрации в процедуре идентификации нелинейных систем в виде ядер Вольтерра

They are researched inaccuracy of the compensation method of identifications of the nonlinear dynamic system in the manner of Volterra kernels. For increasing of computing stability of the method to identifications are used procedures suppression of the noise, founded on wavelet-transformation.Исследуются погрешности компенсационного метода идентификации нелинейных динамических систем в виде ядер Вольтерра. Для повышения вычислительной устойчивости метода идентификации применяются процедуры шумоподавления, основанные на вейвлет–преобразованиях.Досліджуються погрішності компенсаційного методу ідентифікації нелінійних динамічних систем у вигляді ядер Вольтерра. Для підвищення обчислювальної стійкості методу ідентифікації застосовуються процедури шумозаглушення, засновані на вейвлет-перетвореннях

The toolkit for nonparametric identification nonlinear dynamical systems based on Volterra models in frequency domain

The software-hardware tools used for nonlinear dynamical systems nonparametric identification based on Volterra models in frequency domain are presented. The polyharmonic test impacts are selected as the test ones. The proposed methodology and the toolkit are used for building the communication channel model.Представлено програмно-апаратні засоби, що використовуються для непараметричної ідентифікації нелінійних динамічних систем на основі моделей Вольтерра в частотній області. В якості тестових впливів обрано полігармонічні сигнали. Запропонована методологія та інструментарій використовуються для побудови моделі каналу зв’язку

Linear and nonlinear Model Predictive Control (MPC) for regulating pedestrian flows with discrete speed instructions

Airports, shopping malls, stadiums, and large venues in general, can become congested and chaotic at peak times or in emergency situations. Linear Model Predictive Control (MPC) is an effective technology in generating dynamic speed or distance instructions for regulating pedestrian flows, and constitutes a promising interventional technique to improve safety and evacuation time during emergency egress operations. We compare linear and nonlinear MPC controllers and study the influence of using continuous vs. discrete control actions. We aim to evaluate the efficacy of simple instructions that pedestrians can easily follow during evacuations. Linear and Nonlinear AutoRegressive eXogenous models (ARX and NLARX) for prediction are identified from input?output data from strategically designed microscopic evacuation simulations. A microscopic simulation framework is used to design and validate different MPC controllers tuned and refined using the identified models. We evaluate the prediction models? performance and study how the controlled variable type, density, or crowd-pressure, influences the controllers? performance. As a relevant contribution, we show that MPC control with discrete instructions is ideally suited to design and deploy practical pedestrian flow control systems. We found that an adequate size of the set of speed instructions is critical to obtain a good balance between controllability and performance, and that density output control is preferred over crowd-pressure.Universidad de Alcal

A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems