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    Wyświetlanie 1-5 z 5
    Tytuł:
    Work Efficiency Prediction of Persons Working in Traffic Noise Environment Using Adaptive Neuro Fuzzy Inference System (ANFIS) Models
    Autorzy:
    Yadav, Manoj
    Tandel, Bhaven
    Powiązania:
    https://bibliotekanauki.pl/articles/2141713.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic noise
    noise exposure
    social questionnaire survey
    human work efficiency
    ANFIS prediction model
    Opis:
    A study was carried to assess the effect of traffic noise pollution on the work efficiency of shopkeepers in Indian urban areas. For this, an extensive literature survey was done on previous research done on similar topics. It was found that personal characteristics, noise levels in an area, working conditions of shopkeepers, type of task they are performing are the most significant factors to study effects on work efficiency. Noise monitoring, as well as a questionnaire survey, was done in Surat city to collect desired data. A total of 17 parameters were considered for assessing work efficiency under the influence of traffic noise. It is recommended that not more than 6 parameters should be considered for ANFIS modeling hence, before opting for the ANFIS modeling, most affecting parameters to work efficiency under the influence of traffic noise, was chosen by Structural Equation Model (SEM). As a result of the SEM model, two ANFIS prediction models were developed to predict the effect on work efficiency under the influence of traffic noise. R squared for model 1, for training data was 0.829 and for testing data, it was 0.727 and R squared for model 2 for training data was 0.828 and for testing data, it was 0.728. These two models can be used satisfactorily for predicting work efficiency under traffic noise environment for open shutter shopkeepers in tier II Indian cities.
    Źródło:
    Archives of Acoustics; 2021, 46, 4; 677-683
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multicriteria optimization of medical institutions’ schedules on the basis of neuro fuzzy models and evolutionary algorithms
    Autorzy:
    Tkachenko, R.
    Kovalyshyn, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/410857.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    scheduling of medical institutions
    optimization of schedules
    evolutionary algorithms
    multicriteria assessment
    neuro fuzzy models
    Opis:
    Taking into account the expansion of infrastructure and the growth of hospitals, as well as the increase in the influx of patients, the manual preparation of therapies, in particular, regenerative therapy, becomes ineffective and causes frequent dissatisfaction and complaining of patients. Taking into account the large number of factors forming the schedule, the task of multicriteria optimization is presented in accordance with strict restrictions and immediate wishes of patients. This task can be decomposed into several subtasks that require development of: a reference schedule that would satisfy the strict restrictions imposed by the domain; a method for evaluating the reference schedule and intermediate schedules; the method of optimization of the reference scheduling in order to improve the estimated results. In the course of solving these problems it is necessary: to carry out the construction of relevant criteria for evaluating the quality of the decomposition and turn their qualitative values into quantitative forms; carry out the transition from multi-criteria optimization to one-criterion by minimizing the set of evaluation criteria in the scalar value that can be used in the process of optimization; to avoid local optimum and reach the global optimal solution. The article is devised a method of multicriteria assessment and optimization of medical institutions’ schedules, based on the use of automatic theory to construct the reference scheduling of the functioning of the clinic, the application of methods and means of fuzzy logic and evolutionary algorithms. Using an automated system of construction, multicriteria assessment and optimization of schedules of medical institutions can reduce the amount of manual work, as well as increase the level of satisfaction of patients with the quality of regenerative therapy.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 3; 53-59
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Merging of fuzzy models for neuro-fuzzy systems
    Scalanie modeli rozmytych w systemach neuronowo-rozmytych
    Autorzy:
    Simiński, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/375698.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neuro-fuzzy
    fuzzy set
    rule merging
    similarity
    ANNBFIS
    Opis:
    The merging of fuzzy model is widely used for reduction of rule number in fuzzy model. The supernumerosity of rules is mainly caused by grid partition of input domain. In the paper different cause for model merging is described. It is the need for creation of fuzzy model for large data set. In our solution the models are build basing data subset and then the submodels are merged into one. This approach enables quicker elaboration of submodels with relatively good knowledge generalisation ability without waiting for the whole data set to be processed. With passing time, the subsequent submodels are created and merged to create the better model.
    Artykuł opisuje scalanie modeli rozmytych w systemach neuronowo-rozmytych wykorzystywane przy tworzeniu modeli dla dużych zbiorów danych. Nieraz zbiory danych są tak duże, że nie jest możliwe wypracowanie modelu od razu dla całego zbioru. Tworzy się zatem modele dla podzbiorów zbioru danych. Uzyskane w ten sposób modele są następnie scalane, by wypracować jeden model. Podejście to jest także korzystne, gdy wszystkie dane nie są dostępne, ale są dostarczane partiami. Wtedy wstępny model jest wypracowany zanim wszystkie dane zostaną dostarczone do systemu. Artykuł przedstawia sposób wyznaczania podobieństwa reguł w modelu rozmytym oraz opisuje system neuronowo-rozmyty budujący i scalający modele wypracowane dla podzbiorów.
    Źródło:
    Theoretical and Applied Informatics; 2011, 23, 2; 107-126
    1896-5334
    Pojawia się w:
    Theoretical and Applied Informatics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Emotional learning based intelligent speed and position control applied to neurofuzzy model of switched reluctance motor
    Autorzy:
    Rouhani, H.
    Sadeghzadeh, A.
    Lucas, C.
    Araabi, B. N.
    Powiązania:
    https://bibliotekanauki.pl/articles/969753.pdf
    Data publikacji:
    2007
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    intelligent control
    emotion based learning
    neuro-fuzzy models
    switched reluctance motor
    Opis:
    In this paper, rotor speed and position of a Switched Reluctance Motor (SRM) are controlled using an intelligent control algorithm. The controller is working based on a PID signal while its gain is permanently tuned by means of an Emotional Learning Algorithm to achieve a better control performance. Here, nonlinear characteristic of SRM is identified using an efficient training algorithm (LoLiMoT) for Locally Linear Neurofuzzy Model as an unspecified nonlinear plant model. Then, the Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied to the obtained model. While the intelligent controller works based on a computational model of a limbic system in the mammalian brain, its contribution is to improve the performance of a classic controller like PID without much more control effort. The results demonstrate excellent improvements of control action in different working situations.
    Źródło:
    Control and Cybernetics; 2007, 36, 1; 75-95
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adaptive control of cluster-based Web systems using neuro-fuzzy models
    Autorzy:
    Zatwarnicki, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/331413.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model rozmyto neuronowy
    dystrybucja żądań
    klaster serwerów
    QoWS
    neuro fuzzy model
    request distribution
    web cluster
    Opis:
    A significant development of Web technologies requires the application of more and more complex systems and algorithms for maintaining high quality of Web services. Presently, not only simple decision-making tools but also complex adaptation algorithms using artificial intelligence techniques are applied for controlling HTTP request traffic. The paper presents a new LFNRD (Local Fuzzy-Neural Adaptive Request Distribution) algorithm for request distribution in cluster-based Web systems using neuro-fuzzy models of Web servers in the decision-making process. The neuro-fuzzy model which is applied is discussed in detail and a design of the Web switch using the proposed solution is presented. Finally, a testbed is described and the results of a comparative simulation study on the LFNRD algorithm, and other algorithms known from the literature and used in the industry, are presented and discussed.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 365-377
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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