Lane recognition self-learning scheme of mobile robot based on integrated perception system

2013 ◽  
Author(s):  
Yang Yi ◽  
Zhu Hao ◽  
Fu Meng-yin ◽  
Wang Mei-ling
Keyword(s):  
Mobile Robot ◽  
Learning Scheme ◽  
Perception System ◽  
Lane Recognition ◽  
Self Learning

On the self-learning scheme of Nagy and Shelton

1967 ◽  
Vol 55 (10) ◽  
pp. 1764-1765 ◽  
Author(s):  
Y.C. Ho ◽  
A.K. Agrawala
Keyword(s):  
The Self ◽  
Learning Scheme ◽  
Self Learning

scholarly journals Dynamic Residential Energy Management for Real-Time Pricing

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2562
Author(s):  
Leehter Yao ◽  
Fazida Hanim Hashim ◽  
Chien-Chi Lai
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Smart Home ◽  
Solar Power ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Learning Scheme ◽  
Home Energy Management ◽  
Self Learning

A home energy management system (HEMS) was designed in this paper for a smart home that uses integrated energy resources such as power from the grid, solar power generated from photovoltaic (PV) panels, and power from an energy storage system (ESS). A fuzzy controller is proposed for the HEMS to optimally manage the integrated power of the smart home. The fuzzy controller is designed to control the power rectifier for regulating the AC power in response to the variations in the residential electric load, solar power from PV panels, power of the ESS, and the real-time electricity prices. A self-learning scheme is designed for the proposed fuzzy controller to adapt with short-term and seasonal climatic changes and residential load variations. A parsimonious parameterization scheme for both the antecedent and consequent parts of the fuzzy rule base is utilized so that the self-learning scheme of the fuzzy controller is computationally efficient.

Hardware Feedback Self-Organizing Map and its Application to Mobile Robot Location Identification

2007 ◽  
Vol 11 (8) ◽  
pp. 937-945 ◽  
Author(s):  
Hiroomi Hikawa ◽  
◽  
Kazutoshi Harada ◽  
Takenori Hirabayashi ◽  
Keyword(s):  
Mobile Robot ◽  
Weight Vector ◽  
Hardware Architecture ◽  
The Self ◽  
Sensor Data ◽  
Parallel Structure ◽  
Self Organizing Map ◽  
Cluster Data ◽  
Self Learning ◽  
Self Organizing

We propose new hardware architecture for the self-organizing map (SOM) and feedback SOM (FSOM). Due to the parallel structure in the SOM and FSOM algorithm, customized hardware considerably speeds-up processing. Proposed hardware FSOM identifies the location of a mobile robot from a sequence of direction data. The FSOM is self-trained to cluster data to identify where the robot is. The proposed FSOM design is described in C and VHDL, and its performance is tested by simulation using actual sensor data from an experimental mobile robot. Results show that the hardware FSOM succeeds in self-learning to find the robot’s location. The hardware FSOM is estimated to process 6,992 million weight-vector elements per second.

scholarly journals Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

2020 ◽  
Vol 14 ◽  
Author(s):  
Sergey A. Lobov ◽  
Alexey N. Mikhaylov ◽  
Maxim Shamshin ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Spiking Neural Network ◽  
Spatial Properties ◽  
Self Learning
Sign in / Sign up

Export Citation Format

Share Document