[1]
|
Adaptive ADALINE Robust Training Algorithm Under the Maximum Correntropy Criterion With Variable Center
|
|
2022 |
|
|
[2]
|
Adaptive identification under the maximum correntropy criterion with variable center
|
|
RADIOELECTRONIC AND COMPUTER …,
2022 |
|
|
[3]
|
Методи інтелектуальної обробки просторових даних в геоінформаційних системах екологічного моніторингу
|
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2021 |
|
|
[4]
|
Developing a Multi-Step Recurrent Algorithm to Maximize the Criteria of Correntropy
|
|
2021 |
|
|
[5]
|
ADALINE Robust Multistep Training Algorithm
|
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Control systems & computers,
2020 |
|
|
[6]
|
Studying the Properties of a Robust Algorithm for Identifying Linear Objects, Which Minimizes a Combined Functional
|
|
2020 |
|
|
[7]
|
Function Approximation in Mechanics Using Non-Neural and Neural Methods
|
|
2020 |
|
|
[8]
|
Robust identification of non-stationary objects with nongaussian interference
|
|
2019 |
|
|
[9]
|
Аппроксимация функций с помощью нейронных сетей и нечетких систем
|
|
2018 |
|
|
[10]
|
Processing of Noisy Digital Images with Use of Evolving Autoencoders
|
|
2017 |
|
|
[11]
|
Modern neural methods for function approximation
|
|
Issues in the Use of Neural Networks in Information Retrieval,
2017 |
|
|
[12]
|
Обработка зашумленных цифровых изображений с применением эволюционирующих автоэнкодеров
|
|
2017 |
|
|
[13]
|
Алгоритм нечеткой классификации вертикальных элементов строки для сжатия изображения текста
|
|
2016 |
|
|
[14]
|
Speaker Identification and Spoken word Recognition In Noisy Background using Artificial Neural Networks
|
|
International Conference on Electrical, Electronics, and Optimization Techniques,
2016 |
|
|
[15]
|
An Effective Solution to Regression Problem by RBF Neuron Network
|
|
International Journal of Operations Research and Information Systems (IJORIS),
2015 |
|
|
[16]
|
Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects
|
|
Cybernetics and Systems Analysis,
2014 |
|
|
[17]
|
РОБАСТНАЯ НЕЙРОЭВОЛЮЦИОННАЯ ИДЕНТИФИКАЦИЯ НЕЛИНЕЙНЫХ НЕСТАЦИОНАРНЫХ ОБЪЕКТОВ
|
|
Кибернетика и системный анализ,
2014 |
|
|
[18]
|
АДАПТИВНОЕ МАСШТАБИРОВАНИЕ ШАГА ДИСКРЕТИЗАЦИИ ВХОДНЫХ ДАННЫХ ПРИ АППРОКСИМАЦИИ ФУНКЦИЙ С ПОМОЩЬЮ РБФ
|
|
2013 |
|
|
[19]
|
Robust Multiobjective Identification of Nonlinear Objects Based on Evolving Radial Basis Networks
|
|
Journal of Automation and Information Sciences,
2013 |
|
|
[20]
|
Robust identification of nonlinear objects with the help of an evolving radial basis network
|
|
Cybernetics and Systems Analysis,
2013 |
|
|
[21]
|
Робастная идентификация нелинейных объектов с помощью эволюционирующей радиально-базисной сети
|
|
Кибернетика и системный анализ,
2013 |
|
|
[22]
|
Radial basic networks M-training by asymmetric influence functions
|
|
Journal of Automation and Information Sciences,
2012 |
|
|
[23]
|
М-ОБУЧЕНИЕ ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ
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Обчислювальний ?нтелект ,
2011 |
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