Fast neural learning and control of discrete-time nonlinear systems

1995 ◽  
Vol 25 (3) ◽  
pp. 478-488 ◽  
Author(s):  
Liang Jin ◽  
P.N. Nikiforuk ◽  
M.M. Gupta
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Neural Learning ◽  
And Control

System Identification and Control using Probabilistic Incremental Program Evolution Algorithm

2000 ◽  
Vol 12 (6) ◽  
pp. 675-681 ◽  
Author(s):  
Yuehui Chen ◽  
◽  
Shigeyasu Kawaji
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Prior Knowledge ◽  
Discrete Time ◽  
Control Method ◽  
Fitness Function ◽  
Operating Conditions ◽  
Learning Ability ◽  
Program Evolution ◽  
And Control

An indispensable ability for intelligent control is to comprehend and learn about plants, disturbances, environment, and operating conditions. In this paper, the Probabilistic Incremental Program Evolution (PIPE) algorithm, with its self-organizing and learning ability, is used as a promising tool for such purposes. The previous work on evolutionary control by using tree structure based evolutionary algorithm was inverse control in general. In this case, Genetic Programming (GP) was usually used to evolving a directly control law of nonlinear systems. It is difficult to design a better fitness function that should reflect the characteristics of nonlinear systems, and a prior knowledge about operating conditions is usually needed. In this paper, a new identification and control method is proposed without prior knowledge of the plant. Firstly, the input-output behavior of the discrete-time nonlinear system is approximated by the individual structure of PIPE (PIPE Emulator). Secondly, a model based evolutionary controller (PIPE Emulator-based controller) of nonlinear system is designed. Simulation results for a typical nonlinear discrete-time system show the feasibility and effectiveness of the proposed method.

IDENTIFICATION AND CONTROL OF DISCRETE-TIME NONLINEAR SYSTEMS USING AFFINE SUPPORT VECTOR MACHINES

2009 ◽  
Vol 18 (06) ◽  
pp. 929-947 ◽  
Author(s):  
LI ZHANG ◽  
YU-GENG XI ◽  
WEI-DA ZHOU
Keyword(s):  
Support Vector Machine ◽  
Nonlinear Systems ◽  
Discrete Time ◽  
Nonlinear Models ◽  
Support Vector ◽  
Unique Global Solution ◽  
Vector Machines ◽  
And Control ◽  
Nonlinear Autoregressive ◽  
Narx Model

Support vector machine (SVM) is a universal learning method. In this paper, an affine support vector machine (ASVM) for regression is presented for identification and control of input-affine nonlinear models. ASVM is a variant of SVM and so inherits its merits. The solution to ASVM is cast into a convex quadratic programming (QP). Hence ASVM has a unique global solution. In addition, the curse of dimensionality is avoided because ASVM is insensitive to the dimensionality of data. A commonly used model for a nonlinear system is a nonlinear autoregressive exogenous (NARX) model. ASVM could get good performance in both identification and control if a NARX model can be well represented by an input-affine nonlinear model. The experimental results validate the efficiency of ASVM in identification and control of discrete-time nonlinear systems.

scholarly journals A Nearer Optimal and Faster Trained Value Iteration ADP for Discrete-Time Nonlinear Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14933-14944
Author(s):  
Junping Hu ◽  
Gen Yang ◽  
Zhicheng Hou ◽  
Gong Zhang ◽  
Wenlin Yang ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Value Iteration
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