New Approaches to Discrete Modeling of Natural Neural Networks

2018 ◽  
pp. 463-472
Author(s):  
Oleg Kuznetsov ◽  
Ludmila Zhilyakova ◽  
Nikolay Bazenkov ◽  
Boris Boldyshev ◽  
Sergey Kulivets
Keyword(s):  
Neural Networks ◽  
Discrete Modeling ◽  
New Approaches

scholarly journals Genome Attractors as Places of Evolution and Oases of Life

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1646
Author(s):  
Andrzej Kasperski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cytochrome C ◽  
Holistic View ◽  
Discontinuous Process ◽  
New Approaches ◽  
The Neural Networks ◽  
Life Phenomena ◽  
Main Engine

So far, much effort has been made to understand evolution and life phenomena. However, the more we know, the more new puzzles appear. This article introduces some new approaches to understanding what drives evolution. Organism evolution has been examined using artificial neural networks and a semihomologous approach based on the sequences of cytochrome c. To realize this task, three and four-layer neural networks have been designed and then taught. It has been shown that the four-layer neural network more clearly recognizes evolutionary similarities, usually indicating greater (comparing to the three-layer network) similarities to the organisms that were used to train the neural networks. It has been noted that unified cell bioenergetics allows describing the manner in which the main engine that drives evolution works. Reasons for some diseases have been also interpreted to present considerations in a broader and more holistic view. The presented results point out that the evolution of organisms can be considered as a discontinuous process taking place mainly in genome attractors that define and stabilize organisms.

scholarly journals A Novel Technique for Solving Fully Fuzzy Nonlinear Systems Based on Neural Networks

2020 ◽  
Vol 07 (01) ◽  
pp. 93-107 ◽  
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Complex Systems ◽  
Gradient Descent ◽  
Descent Algorithm ◽  
Novel Technique ◽  
New Approaches ◽  
Gradient Descent Algorithm

Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.

Discrete Modeling of Multi-transmitter Neural Networks with Neuronal Competition

2017 ◽  
pp. 10-16 ◽  
Author(s):  
Nikolay Bazenkov ◽  
Varvara Dyakonova ◽  
Oleg Kuznetsov ◽  
Dmitri Sakharov ◽  
Dmitry Vorontsov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discrete Modeling
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