Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems
Autor: | J. R. P. Gupta, Amit Mohindru, Smriti Srivastava, Rajesh Kumar |
---|---|
Rok vydání: | 2018 |
Předmět: |
Lyapunov stability
0209 industrial biotechnology Radial basis function network Adaptive control Artificial neural network Computer science Applied Mathematics 020208 electrical & electronic engineering Stability (learning theory) 02 engineering and technology Computer Science Applications Noise Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Control theory Convergence (routing) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation |
Zdroj: | ISA transactions. 87 |
ISSN: | 1879-2022 |
Popis: | In this paper, a novel temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems is proposed. The proposed structure consists of recurrent hidden neurons having weighted self-feedback loops and a weighted linear feed-through from the input layer directly to the output layer neuron(s). The dynamic back-propagation algorithm is developed and used for updating the parameters of the proposed structure. To improve the performance of learning algorithm, discrete Lyapunov stability method is used to develop an adaptive learning rate scheme. This scheme ensures the faster convergence of the parameters and maintains the stability of the system. A total of 5 complex nonlinear systems are used to test and compare the performance of the proposed network with other neural network structures. The disturbance rejection tests are also carried out to check whether the proposed scheme is able to handle the external disturbance/noise signals effects or not. The obtained results show the efficacy of the proposed method. |
Databáze: | OpenAIRE |
Externí odkaz: |