Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems

2020 ◽  
Vol 89 ◽  
pp. 103447 ◽  
Author(s):  
F. Baghbani ◽  
M.-R. Akbarzadeh-T ◽  
M.-B. Naghibi-Sistani ◽  
Alireza Akbarzadeh
Keyword(s):  
Neural Networks ◽  
Nonlinear Control ◽  
Control Systems ◽  
Approximation Property ◽  
Nonlinear Control Systems ◽  
Universal Approximation ◽  
Adaptive Nonlinear Control

Adaptive nonlinear control systems for atmospheric correction

1993 ◽  
Author(s):  
Allan Wirth ◽  
Andrew J. Jankevics ◽  
Sol W. Gully ◽  
Michael Athans ◽  
James Huang
Keyword(s):  
Nonlinear Control ◽  
Control Systems ◽  
Atmospheric Correction ◽  
Nonlinear Control Systems ◽  
Adaptive Nonlinear Control

Neural Networks and Identification of Systems With Unobserved States

1993 ◽  
Vol 115 (1) ◽  
pp. 196-203 ◽  
Author(s):  
C. J. Goh ◽  
Lyle Noakes
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control System ◽  
Nonlinear Control ◽  
Control Systems ◽  
Output Feedback ◽  
Original System ◽  
Nonlinear Control Systems ◽  
Output Feedback Controller ◽  
Network Emulator

Consider a nonlinear control system, whose structure is not known (apart from the order of the system) and whose states are not observed. We observe the output of the system for a period of time using persistently exciting input, and use the observation to train a neural network emulator whose output approximates that of the original system. We point out that such an explicit dynamical relationship between the input and the output is useful for the purpose of construction of output feedback controller for nonlinear control systems. Specialization of the method to linear systems allows swift convergence and parameter identification in some cases.

Experiments with adaptive nonlinear control systems for atmospheric correction

1994 ◽  
Author(s):  
Sol W. Gully ◽  
James Huang ◽  
Nikolaos Denis ◽  
Douglas P. Looze ◽  
Allan Wirth ◽  
...  
Keyword(s):  
Nonlinear Control ◽  
Control Systems ◽  
Atmospheric Correction ◽  
Nonlinear Control Systems ◽  
Adaptive Nonlinear Control

Nonlinear Control Systems

2004 ◽  
Author(s):  
Christopher I. Byrnes ◽  
Alberto Isidori
Keyword(s):  
Nonlinear Control ◽  
Control Systems ◽  
Nonlinear Control Systems

NORMAL FORMS FOR FUZZY LOGIC — AN APPLICATION OF KOLMOGOROV’S THEOREM

1996 ◽  
Vol 04 (04) ◽  
pp. 331-349 ◽  
Author(s):  
VLADIK KREINOVICH ◽  
HUNG T. NGUYEN ◽  
DAVID A. SPRECHER
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Normal Form ◽  
Normal Forms ◽  
Approximation Property ◽  
Universal Approximation ◽  
Approximation Result ◽  
Logical Operations ◽  
Kolmogorov's Theorem

This paper addresses mathematical aspects of fuzzy logic. The main results obtained in this paper are: 1. the introduction of a concept of normal form in fuzzy logic using hedges; 2. using Kolmogorov’s theorem, we prove that all logical operations in fuzzy logic have normal forms; 3. for min-max operators, we obtain an approximation result similar to the universal approximation property of neural networks.

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