Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, a long-team immersion of concrete in dilute sulfuric acid is carried out. On the basis of the experimental data, a time serious prediction model of concrete corrosion in sulfuric based on support vector machine (SVM) is developed. The design steps and learning algorithm are also given. Comparing with the test result, this model has good predictive function, with the suitable reconstructed...
At present, the collaborative relationships between enterprises have a significant impact on operating efficiency for the supply chain. Base on Bayesian Network theory, the paper establishes a model of collaborative sensitive index of enterprises in the supply chain by adopting methods of genetic algorithms, and introduces collaborative programs that can improve the efficiency of the entire supply...
A fractional order PID (FOPID) controller to damp inter area oscillations of power systems is studied. FOPID is a PID whose derivative and integral orders are fractional rather than integer. The difficulty of design a FOPID controller is how to determine the five key parameters of FOPID. This paper uses genetic algorithm particle swarm optimization (GAPSO) algorithm to design the FOPID controller...
This paper proposes a layered hybrid ant colony and genetic algorithm to solve the multi-objective optimization of dynamic job-shop scheduling problem in manufacturing grid. This algorithm is constructed in a layered structure, where the out layer uses ant colony algorithm to select the machine and the inner layer uses genetic algorithm with neighborhood search to optimize the job scheduling. We use...
An approach for mobile robot path planning based on particle filter is proposed. Ferguson splines have been used as a path description to ensure the smoothness of the path. Supposing the best path as the true state, and the others as states polluted by noise, the problem of searching for the best path has been transferred to a filter problem. Therefore the particle filter algorithm is used to solve...
Taking the full network observability of power system operation states and the least number of phasor measurement units (PMUs) as an objective function, an improved optimal PMU placement algorithm is proposed. In this algorithm, genetic algorithm (GA) is effectively combined with the particle swarm optimization (PSO) algorithm to ensure that the optimal solution can be obtained. The cross and aberrance...
Though deeply analyzing and comparing the mechanism of genetic algorithm and reinforcement learning, a novel algorithm for controlling genetic algorithms using reinforcement learning named SCGA, is proposed and analyzed theoretically. In the existing similar method RL-GA, a reinforcement learning agent uses Q(lambda)-learning to control genetic algorithms. Two problems with such method are that, (1)...
This paper proposes two models for predicting the completion time of jobs in a service Grid. The single service model predicts the completion time of a job in a Grid that provides only one type of service. The multiple services model predicts the completion time of a job that runs in a Grid which offers multiple types of services. We have developed two algorithms that use the predictive models to...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.