scholarly journals Sage Revised Reiterative Even Zernike Polynomials Neural Network Control with Modified Fish School Search Applied in SSCCRIM Impelled System

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1760
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Network Control ◽  
Learning Ability ◽  
Zernike Polynomials ◽  
Control Performance ◽  
Fish School ◽  
Learning Rates ◽  
Variable Transmission ◽  
Fine Control ◽  
Learning Law

In light of fine learning ability in the existing uncertainties, a sage revised reiterative even Zernike polynomials neural network (SRREZPNN) control with modified fish school search (MFSS) method is proposed to control the six-phase squirrel cage copper rotor induction motor (SSCCRIM) impelled continuously variable transmission assembled system for obtaining the brilliant control performance. This control construction can carry out the SRREZPNN control with the cozy learning law, and the indemnified control with an assessed law. In accordance with the Lyapunov stability theorem, the cozy learning law in the revised reiterative even Zernike polynomials neural network (RREZPNN) control can be extracted, and the assessed law of the indemnified control can be elicited. Besides, the MFSS can find two optimal values to adjust two learning rates with raising convergence. In comparison, experimental results are compared to some control systems and are expressed to confirm that the proposed control system can realize fine control performance.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

Fuzzy Neural Network Hybrid Learning Control on AUV

2012 ◽  
Vol 468-471 ◽  
pp. 1732-1735
Author(s):  
Jing Zhao ◽  
Zhao Lin Han ◽  
Yuan Yuan Fang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Network Control ◽  
Learning Ability ◽  
B Spline ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

Wheeled Robot Based on Fuzzy Neural Network Tracking Studies

2013 ◽  
Vol 373-375 ◽  
pp. 181-184
Author(s):  
Su Ying Zhang ◽  
Shao Jie Xu ◽  
Jing Fei Zhu ◽  
Bing Hao Li ◽  
Wen Pan Shi
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
External Disturbance ◽  
Network Control ◽  
Learning Ability ◽  
Parameter Uncertainties ◽  
Wheeled Robot ◽  
Fuzzy Neural ◽  
Arbitrary Precision

The wheeled robot with non-integrity constraints is a typical nonlinear system, in order to achieve the ideal path tracing, presented a theory based on fuzzy neural network control. Centralized compensation system based on neural network uncertainty can be arbitrary-precision approximation of continuous nonlinear functions as well as the complex uncertainties with adaptive and learning ability. By MATLAB simulation showed that the control method to ensure fast convergence and error robustness of parameter uncertainties and external disturbance.

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Artificial Bee Colony ◽  
Continuously Variable Transmission ◽  
Control Performance ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Functional Expansions ◽  
Variable Transmission ◽  
Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.

NEURAL NETWORK CONTROL SYSTEMS THAT LEARN TO PERFORM APPROPRIATELY

2001 ◽  
Vol 11 (01) ◽  
pp. 79-88 ◽  
Author(s):  
JOHN A. BULLINARIA ◽  
PATRICIA M. RIDDELL
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Network Control ◽  
Oculomotor Control ◽  
Evolution And Development ◽  
Learning Rates ◽  
Network Control Systems ◽  
The Cost

Setting up a neural network with a learning algorithm that determines how it can best operate is an efficient way to formulate control systems for many engineering applications, and is often much more feasible than direct programming. This paper examines three important aspects of this approach: the details of the cost function that is used with the gradient descent learning algorithm, how the resulting system depends on the initial pre-learning connection weights, and how the resulting system depends on the pattern of learning rates chosen for the different components of the system. We explore these issues by explicit simulations of a toy model that is a simplified abstraction of part of the human oculomotor control system. This allows us to compare our system with that produced by human evolution and development. We can then go on to consider how we might improve on the human system and apply what we have learnt to control systems that have no human analogue.

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