Zobrazeno 1 - 10
of 77
pro vyhledávání: '"J. R. P. Gupta"'
Publikováno v:
Soft Computing. 23:101-114
In this paper, a comparative study is performed to test the approximation ability of different neural network structures. It involves three neural networks multilayer feedforward neural network (MLFFNN), diagonal recurrent neural network (DRNN), and
Publikováno v:
Neurocomputing. 287:102-117
This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a simpler structure. Thereafter, we have developed a
Publikováno v:
Arabian Journal for Science and Engineering. 43:2971-2993
This paper performs the comparative study of two feed-forward neural networks: radial basis function network (RBFN), multilayer feed-forward neural network (MLFFNN) and a recurrent neural network: nonlinear auto-regressive with exogenous inputs (NARX
Publikováno v:
Soft Computing. 21:4465-4480
This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynam
Publikováno v:
Soft Computing. 21:4447-4463
In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structur
Publikováno v:
Neural Computing and Applications. 30:223-239
Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The oth
Publikováno v:
International Journal of Adaptive Control and Signal Processing.
Publikováno v:
ISA transactions. 87
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
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9789811031526
FICTA (1)
FICTA (1)
A procedure based on the use of radial basis function network (RBFN) is presented for black box modeling of nonlinear dynamical systems. The generalization ability of RBFN is invoked to approximate the mathematical model of the given unknown nonlinea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d40e1970c51bb69dfc4c4ebf555b059d
https://doi.org/10.1007/978-981-10-3153-3_40
https://doi.org/10.1007/978-981-10-3153-3_40
Publikováno v:
Computational Intelligence. 31:106-131
This research proposes a pattern/shape-similarity-based clustering approach for time series prediction. This article uses single hidden Markov model HMM for clustering and combines it with soft computing techniques fuzzy inference system/artificial n