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Wyszukujesz frazę "neuro-adaptive learning" wg kryterium: Wszystkie pola


Wyświetlanie 1-3 z 3
Tytuł:
A Neuro-Adaptive Learning (NAL) Approach about Costs of Residential Buildings
Autorzy:
Ugur, L.
Powiązania:
https://bibliotekanauki.pl/articles/1031585.pdf
Data publikacji:
2017-09
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
costs of residential buildings
neuro-adaptive learning
fuzzy logic
Opis:
The artificial neural networks and fuzzy logic models are two well-known branches of artificial intelligence and have been broadly and successfully used to simulate input-output systems. Over the last two decades, a different modeling method based on fuzzy logic or neural networks has become popular and has been used by many researchers for a variety of engineering applications. Nowadays, for reducing the amount of experiment costs, modeling methods based on artificial neural networks and fuzzy logic systems have become more popular and have been used by many researchers for many civil engineering management applications. In this study a neuro-adaptive learning approach about costs of residential buildings was designed. As a result, NAL can be an alternative approach for the evaluation of the cost estimations of residential buildings construction.
Źródło:
Acta Physica Polonica A; 2017, 132, 3; 585-587
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning
Autorzy:
Bodyanskiy, Yevgeniy V.
Tyshchenko, Oleksii K.
Powiązania:
https://bibliotekanauki.pl/articles/330840.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
data stream
membership function
training procedure
adaptive neuro-fuzzy system
extended neo-fuzzy neuron
strumień danych
funkcja przynależności
neuronowo rozmyty układ adaptacyjny
Opis:
This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 477-488
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade
Autorzy:
Jasti, Sireesha
Kumar, G.V.S. Raj
Powiązania:
https://bibliotekanauki.pl/articles/2200961.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
deep learning network
feedback artificial tree
natural language processing (NLP)
rider optimization algorithm
sentiment grade classification
Opis:
Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 1; 37--50
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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