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ISA Trans. 2019 Apr;87:88-115. doi: 10.1016/j.isatra.2018.11.027. Epub 2018 Dec 4.

Temporally local recurrent radial basis function network for modeling and adaptive control of nonlinear systems.

Author information

1
Department of Instrumentation and Control Engineering, Bharati Vidyapeeth College of Engineering, A-4, Paschim Vihar, New Delhi 110063, India. Electronic address: rajeshmahindru23@gmail.com.
2
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi 110078, India. Electronic address: smriti.nsit@gmail.com.
3
Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi 110078, India. Electronic address: jairamprasadgupta@gmail.com.
4
Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology, New Delhi 110020, India. Electronic address: amitmohindru26@gmail.com.

Abstract

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.

KEYWORDS:

Adaptive learning rate; Dynamic back-propagation; Lyapunov stability method; Nonlinear system identification and adaptive control; Temporally local recurrent radial basis function network

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