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Abstract. This paper presents a type of recurrent artificial neural network architecture for identification of an arbitrary, continuous dynamic system.
This paper presents a type of recurrent artificial neural network architecture for iden- tification of an arbitrary, continuous dynamic system. The recurrent ...
Abstract: A dynamic recurrent neural network. (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to.
The proposed recurrent artificial neural network architecture has significant advantages over similar models in continuous time nonlinear system ...
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Sep 7, 2024 · A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and ...
The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is ...
As mentioned in Chapter 1, neural networks can be classified as feedforward networks and recurrent networks. In feedforward networks, the processing ...
Jan 21, 2022 · This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems.
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems.
This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems.