A NOVEL FORWARD–BACKWARD SMOOTHING-BASED LEARNING SUBSPACE METHOD FOR RECOGNITION OF RADAR TARGETS

1999 ◽  
Vol 13 (01) ◽  
pp. 65-83 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
High Resolution ◽  
Selection Method ◽  
Convergence Speed ◽  
Correct Recognition ◽  
Subspace Method ◽  
Training Samples ◽  
High Resolution Radar ◽  
Radar Targets ◽  
Order Of Presentation ◽  
Better Than

This paper proposes a novel Forward–Backward Smoothing-Based Learning Subspace Method (FBSLSM), which can satisfy the equirements of being insensitive to the order of presentation of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.

REDUCTION OF ASPECT DEPENDENT SPECKLE FLUCTUATIONS IN HIGH-RESOLUTION RADAR RANGE PROFILES

2010 ◽  
Vol 69 (8) ◽  
pp. 687-698 ◽  
Author(s):  
V. M. Orlenko ◽  
P. A. Molchanov ◽  
A. V. Totsky ◽  
Karen O. Egiazarian ◽  
J. T. Astola
Keyword(s):  
High Resolution ◽  
High Resolution Radar

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056009
Author(s):  
Guangyu Zhou ◽  
Aijia Ouyang ◽  
Yuming Xu
Keyword(s):  
Face Recognition ◽  
Cloud Model ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Convergence Speed ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

Fabrication of Magnetic Tips for High-Resolution Magnetic Force Microscopy

2013 ◽  
Vol 543 ◽  
pp. 35-38 ◽  
Author(s):  
Masaaki Futamoto ◽  
Tatsuya Hagami ◽  
Shinji Ishihara ◽  
Kazuki Soneta ◽  
Mitsuru Ohtake
Keyword(s):  
Magnetic Material ◽  
High Resolution ◽  
Magnetic Force Microscopy ◽  
Magnetic Force ◽  
Magnetic Force Microscope ◽  
Force Microscopy ◽  
High Magnetic Moment ◽  
Tip Radius ◽  
Better Than ◽  
Sputtering System

Effects of magnetic material, coating thickness, and tip radius on magnetic force microscope (MFM) spatial resolution have been systematically investigated. MFM tips are prepared by using an UHV sputtering system by coating magnetic materials on non-magnetic Si tips employing targets of Ni, Ni-Fe, Co, Fe, Fe-B, and Fe-Pd. MFM spatial resolutions better than 9 nm have been confirmed by employing magnetic tips coated with high magnetic moment materials with optimized thicknesses.

Synthesis Design and Analysis of a Hybrid Controller for Robotic Arms

2016 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Pid Controller ◽  
Adaptive Controller ◽  
Convergence Speed ◽  
Model Reference ◽  
Robotic Arms ◽  
Hybrid Controller ◽  
Comparison Results ◽  
Convergence Performance ◽  
Model Reference Adaptive ◽  
Better Than

In this paper, a hybrid controller for robotic arms is proposed and designed by combining a proportional-integral-derivative controller (PID) and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the model reference adaptive controller and the PID+MRAC hybrid controller for 1-DOF and 2-DOF manipulators are compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+MRAC controllers are better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

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