scholarly journals Perception and Action State Space Construction Method with Dynamics-based Self-organizing Incremental Neural Network on Subsumption Architecture

2010 ◽  
Vol 22 (2) ◽  
pp. 266-278
Author(s):  
Fumiaki SAITOH ◽  
Osamu HASEGAWA
Keyword(s):  
Neural Network ◽  
State Space ◽  
Perception And Action ◽  
Construction Method ◽  
Subsumption Architecture ◽  
Space Construction ◽  
Self Organizing

Building up Embodiment in Learning Agents Using A Gaussian Radial Basis Function Neural Network

2000 ◽  
Vol 12 (6) ◽  
pp. 656-663
Author(s):  
Hajime Murao ◽  
◽  
Shinzo Kitamura
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
State Space ◽  
Performance Improvement ◽  
Basis Function ◽  
The State ◽  
Learning Agents ◽  
Radial Basis ◽  
Space Construction ◽  
Hidden Layer

In this paper, we propose actor-critic learning with adaptive state space construction. The gaussian radial basis function neural network is employed for both the actor and the critic modules, where each hidden neuron covers a subspace of the sensor space and so the hidden layer corresponds to the state space. In the proposed algorithm, a robot starts without any states and a new state is generated incrementally by adding a new hidden neuron. One clear advantage of the proposed algorithm to others is the performance improvement by the minimal training after adding a new state, i.e. the adjustment of the connective strength between the new neuron and others is only required after adding a new hidden neuron. This provides an efficient method to construct the state space during the learning in the real world. Resulting state space represents aspects of the environment in which the robot works and the characteristic of the robot itself. In this sense, the obtained state space is said to represent the embodiment of the robot.

Autonomous State Space Construction Method Based on Mixed Normal Distributions for Pedestrian Detection

2018 ◽  
Vol 138 (9) ◽  
pp. 1100-1107
Author(s):  
Yuya Michishita ◽  
Shuto Higashi ◽  
Masatoshi Shibata ◽  
Ryuya Muramatsu ◽  
Hideo Yamada ◽  
...  
Keyword(s):  
State Space ◽  
Pedestrian Detection ◽  
Construction Method ◽  
Normal Distributions ◽  
Space Construction ◽  
Autonomous State

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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