Evolutionary Learning Algorithm for Multi-layer Morphological Neural Networks

2013 ◽  
Vol 12 (4) ◽  
pp. 852-856
Author(s):  
He Chunmei
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Evolutionary Learning ◽  
Morphological Neural Networks

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

scholarly journals Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Elena E. Limonova ◽  
Daniil M. Alfonso ◽  
Dmitry P. Nikolaev ◽  
Vladimir V. Arlazarov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Morphological Neural Networks

FUNCOM: A constrained learning algorithm for fuzzy neural networks

2000 ◽  
Vol 112 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Paris Mastorocostas ◽  
John Theocharis
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Constrained Learning

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

Effective Transfer Learning Algorithm in Spiking Neural Networks

2021 ◽  
pp. 1-13
Author(s):  
Qiugang Zhan ◽  
Guisong Liu ◽  
Xiurui Xie ◽  
Guolin Sun ◽  
Huajin Tang
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Spiking Neural Networks ◽  
Effective Transfer
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