A quick intelligent control learning algorithm based on single adaptive neuron controller

2002 ◽  
Author(s):  
Li Wei ◽  
Li Shiyong ◽  
Lang Li ◽  
Ma Shuqing ◽  
Shen Yi
Keyword(s):  
Intelligent Control ◽  
Learning Algorithm ◽  
Control Learning ◽  
Adaptive Neuron

Neighbor Learning Control: Learning Control for Multiple Subsystems

2007 ◽  
Author(s):  
Manas C. Menon ◽  
H. Harry Asada
Keyword(s):  
Control Systems ◽  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Control Algorithms ◽  
Good Convergence ◽  
Control Techniques ◽  
Difficult Time ◽  
Control Learning ◽  
Build Systems

With the rise of smart material actuators, it has become possible to design and build systems with a large number of small actuators. Many of these actuators exhibit a host of nonlinearities including hysteresis. Learning control algorithms can be used to guarantee good convergence of these systems even in the presence of the nonlinearities. However, they have a difficult time dealing with certain classes of noise or disturbances. We present a neighbor learning algorithm to control systems of this type with multiple identical actuators. In addition, we present a neighbor learning algorithm to control these systems for a certain class of non-identical actuators. We prove that in certain situations these algorithms provide improved convergence when compared to traditional iterative learning control techniques. Simulations results are presented that corroborate our expectations from the proofs.

Shape Control Learning Algorithm for Neural Networks

2013 ◽  
Vol 347-350 ◽  
pp. 2270-2274
Author(s):  
Dai Yuan Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Experiments ◽  
Shape Control ◽  
Learning Algorithm ◽  
Neural System ◽  
Cubic Spline ◽  
New Method ◽  
Shape Parameters ◽  
Control Learning

A new kind of shape control learning algorithm (SCLA) for training neural networks is proposed. We use the rational cubic spline (with quadratic denominator) to implement a new neural system for shape control, and construct a new kind of artificial neural networks based on given patterns. The shape can be controlled by some shape parameters, which is much different from the known algorithms for training neural networks. The numerical experiments indicate that the new method proposed in this paper demonstrates good results.

A Hybrid Intelligent Excitation Control for Ship Power Station

2011 ◽  
Vol 128-129 ◽  
pp. 780-783
Author(s):  
Zhi Peng Shen ◽  
Chen Guo ◽  
Yu Ting Wang
Keyword(s):  
Intelligent Control ◽  
Learning Algorithm ◽  
Synchronous Generator ◽  
Control Technique ◽  
Excitation Control ◽  
Power Station ◽  
Terminal Voltage ◽  
Simulation Results ◽  
Synchronous Generator Excitation ◽  
Generator Excitation Control

Hybrid intelligent control technique is used for ship power station synchronous generator excitation control in this paper. The parameters and structure of the excitation controller are learned and adjusted through a hybrid learning algorithm combining self-organizing learning with BP learning. This algorithm converges much faster than original BP learning. Simulation results show that the synchronous generator excitation controller based on the learning algorithm can significantly stabilize terminal voltage.

scholarly journals Electric Window Regulator Based on Intelligent Control

2021 ◽  
Author(s):  
Yang Xu ◽  
Yun Li ◽  
Chao Li
Keyword(s):  
Intelligent Control ◽  
Learning Algorithm ◽  
Control Function ◽  
Motor Current ◽  
Speed Regulation ◽  
Window Opening ◽  
Regulation Method ◽  
Window Regulator ◽  
The Stability ◽  
Opening And Closing

In order to effectively solve the problem of installation cost of automobile electric windows and the safety of passengers, the window regulator of the car must have an intelligent control function. For example, most automobile windows now have an anti-pinch function. In this paper, the model of DC brushed motor is analyzed, an intelligent control scheme for automotive power windows is proposed, and the relationship between current ripple and window travel, motor current and external resistance are verified. In the hardware design, S9S12G128 is the main control chip, and the motor current acquisition method is designed. In the software design, intelligent control methods such as current integration method, adaptive and self-learning algorithm and intelligent speed regulation method are proposed to realize functions such as automatic window opening and closing, intelligent anti-pinch and intelligent speed regulation. After many experiments, the results prove the feasibility of the above methods and the stability of the system.

Special Issue on Selected Papers in SCIS & ISIS 2004 - No.1

2005 ◽  
Vol 9 (2) ◽  
pp. 91-91
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Image Processing ◽  
Evolutionary Computation ◽  
Intelligent Control ◽  
Intelligent Systems ◽  
System Modeling ◽  
Program Committee ◽  
Special Issue ◽  
Network Applications ◽  
Control Learning

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.

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