An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

SELF-ORGANIZING NEURAL NETWORK MODEL OF THE SENSORY-MOTOR MECHANISM

2000 ◽  
Vol 14 (17) ◽  
pp. 1815-1824
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Autonomous Agents ◽  
Lateral Interactions ◽  
Sensory Motor ◽  
Self Organizing

We describe a new biologically motivated model of the sensory-motor mechanism. The model is based on a self-organizing neural network with modifiable lateral interactions and a "master-slave" connection between the sensorial and motor modules. The results show that the described model is a useful feature that can be exploited by autonomous agents. An example of implementation in the case of a "moving virtual creature" is also presented.

Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession

2004 ◽  
Vol 16 (12) ◽  
pp. 2665-2697 ◽  
Author(s):  
Hiroaki Wagatsuma ◽  
Yoko Yamaguchi
Keyword(s):  
Field Potential ◽  
Computer Experiments ◽  
Cognitive Map ◽  
Phase Precession ◽  
Spatial Environment ◽  
Hippocampal Network ◽  
Theta Phase ◽  
Space Coding ◽  
Temporal Sequences ◽  
Theta Phase Precession

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.

Clustering Urban Multifunctional Landscapes Using the Self-Organizing Feature Map Neural Network Model

2014 ◽  
Vol 140 (2) ◽  
pp. 05014001 ◽  
Author(s):  
Yang Gao ◽  
Zhe Feng ◽  
Yang Wang ◽  
Jin-Long Liu ◽  
Shuang-Cheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
The Self ◽  
Feature Map ◽  
Multifunctional Landscapes ◽  
Self Organizing

Classification and Prediction of Reagents' Roles by FRAU System with Self-Organizing Neural Network Model

2000 ◽  
Vol 73 (9) ◽  
pp. 1955-1965 ◽  
Author(s):  
Hiroko Satoh ◽  
Kimito Funatsu ◽  
Keiko Takano ◽  
Tadashi Nakata
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Self Organizing

scholarly journals A Neural Network Model of Self-Organizing Process in Group Formation

1991 ◽  
Vol 27 (Supplement) ◽  
pp. 114-115
Author(s):  
Shigekazu Ishihara ◽  
Keiko Hatamoto ◽  
Mitsuo Nagamachi ◽  
Yukihiro Matsubara
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Group Formation ◽  
Self Organizing ◽  
Organizing Process
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