Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems

Shobana, R.; Jaint, Bhavnesh; Kumar, Rajesh

doi:10.1007/s00500-023-09187-5

Cited by 5 publications

References 41 publications

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A Bayesian transfer sparse identification method for nonlinear ARX systems

Zhang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

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SummaryIn this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm.

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A Bayesian transfer sparse identification method for nonlinear ARX systems

Zhang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

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ISFNN: an enhanced neural network for parametric modeling of passive devices with input skip-connections

Ren,

Deng,

Zheng

2024

Appl Intell

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A Dynamical Systems Approach to Optimal Foraging

Chaturvedi,

El-Gazzar,

Gerven

2024

Preprint

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Foraging for resources in an environment is a fundamental activity that must be addressed by any biological agent. Thus, modelling this phenomenon in simulations can enhance our understanding of the characteristics of natural intelligence. In this work, we present a novel approach to modelling this phenomenon in silico. We achieve this by using a continuous coupled dynamical system for modelling the system. The dynamical system is composed of three differential equations, representing the position of the agent, the agent's control policy, and the environmental resource dynamics. Crucially, the control policy is implemented as a neural differential equation which allows the control policy to adapt in order to solve the foraging task. Using this setup, we show that when these dynamics are coupled and the controller parameters are optimized to maximize the rate of reward collected, adaptive foraging emerges in the agent. We further show that the internal dynamics of the controller, as a surrogate brain model, closely resemble the dynamics of the evidence accumulation mechanism, which may be used by certain neurons of the dorsal anterior cingulate cortex region in non-human primates, for deciding when to migrate from one patch to another. Finally, we show that by modulating the resource growth rates of the environment, the emergent behaviour of the artificial agent agrees with the predictions of the optimal foraging theory.

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Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems

Cited by 5 publications

References 41 publications

A Bayesian transfer sparse identification method for nonlinear ARX systems

A Bayesian transfer sparse identification method for nonlinear ARX systems

ISFNN: an enhanced neural network for parametric modeling of passive devices with input skip-connections

A Dynamical Systems Approach to Optimal Foraging

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