Name
Title | ||
|---|---|---|
Simple adaptive control for SISO nonlinear systems using neural network based on genetic algorithm |
Abstract | ||
|---|---|---|
This paper presents a method of continuous-time simple adaptive control (SAC) using neural network based on genetic algorithm (GA) for a single-input single-output (SISO) nonlinear systems, bounded-input bounded-output, and bounded nonlinearities. According to the power of neural network and the characteristics of simple adaptive control, constructed a simple adaptive control using neural networks, and in neural network learning process, introduce genetic algorithm, using genetic algorithm to optimize the neural network weights. Simple adaptive control, neural network and genetic algorithm were combined to form Genetic Algorithms-Neural Network Simple Adaptive Control (GA-NNSAC). Finally, the simulation results show that the proposed method has fine accuracy, dynamic character and robustness through computer simulations. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLC.2010.5580615 | ICMLC |
Keywords | Field | DocType |
nonlinear system,neural network,genetic algorithm,neurocontrollers,simple adaptive control,learning (artificial intelligence),siso,bounded nonlinearities,neural network learning process,adaptive control,nonlinear control systems,single-input single-output system,bounded-input bounded-output nonlinearities,genetic algorithms,control nonlinearities,continuous-time control,siso nonlinear systems,algorithm design and analysis,learning artificial intelligence,artificial neural networks,machine learning,optimization,computer simulation | Algorithm design,Single-input single-output system,Control theory,Computer science,Probabilistic neural network,Robustness (computer science),Time delay neural network,Artificial intelligence,Adaptive control,Artificial neural network,Genetic algorithm,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-1-4244-6526-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |