A configurable nonlinear operation unit for neural network accelerator

Cai, Yujie; Li, Xin; Han, Jun; Zeng, Xiaoyang

doi:10.1109/asicon.2017.8252477

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…They applied fuzzy C-means clustering data-driven algorithm and least squares method to identify system. As a significant model of machine learning, neural network has strong nonlinear fitting ability [15][16][17][18][19], which is widely used in dynamic systems. Feedforward neural network [20], convolutional neural network [21], long short-term memory neural network [22,23], echo state network [24,25] are selected for data-driven prediction of various systems.…”

Section: Introductionmentioning

confidence: 99%

Neural network modeling and dynamic behavior prediction of nonlinear dynamic systems

et al. 2023

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In this paper, data-driven models of nonlinear dynamic systems are built through neural network. The numerical solution of state equations is used to simulate the experimental data as well as training data of neural network. The loss function with two coefficients is constructed according to the relationship between the data. Forward Euler method and the trained neural network are combined to predict the response data of different excitation frequencies. Then, whole data-driven modeling and substructure data-driven modeling are presented to model and analyse a fivedegree-of-freedom duffing oscillator. By comparison, the prediction accuracy of substructure data-driven modeling is higher. Furthermore, the influence of loss function, hidden layer, training data and noise on the accuracy of the model is studied.The results show that the selection of training data and the number of hidden layers have a great impact on the prediction ability, and verify accuracy, generalization and robustness of the model. In order to improve the prediction accuracy of substructure data-driven modeling, adjustable learning rate is adopted in the optimizer. The effects of adding control parameters to the network input (APNI) and not adding control parameters to the network input (NAPNI) on the dynamic characteristics are considered respectively. The prediction accuracy of the model can be effectively improved by adding control parameters to the network input.

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Section: Introductionmentioning

confidence: 99%

Neural network modeling and dynamic behavior prediction of nonlinear dynamic systems

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling and Dynamic Behavior Prediction of Nonlinear Dynamic Systems

Zhang

Sun

Wang

et al. 2022

Preprint

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In the complex dynamic problems of practical engineering, the method of establishing dynamic equations and numerical analysis based on theories of mechanics is difficult to meet the needs of high dimension, multi-scale and high precision. In this paper, data-driven models of nonlinear dynamic systems are built through neural network. The numerical solution of state equations is used to simulate the experimental data as well as training data of neural network. The loss function with two coefficients is constructed according to the relationship between the data. Forward Euler method and the trained neural network are combined to predict the response data of different excitation frequencies. Then, whole data-driven modeling and substructure data-driven modeling are presented to model and analyse a five-degree-of-freedom duffing oscillator. By comparison, the prediction accuracy of substructure data-driven modeling is higher. Furthermore, the influence of loss function, hidden layer, training data and noise on the accuracy of the model is studied. The results show that the selection of training data and the number of hidden layers have a great impact on the prediction ability, and verify accuracy, generalization and robustness of the model. In order to improve the prediction accuracy of substructure data-driven modeling, adjustable learning rate is adopted in the optimizer. The effects of adding control parameters to the network input (APNI) and not adding control parameters to the network input (NAPNI) on the dynamic characteristics are considered respectively. The prediction accuracy of the model can be effectively improved by adding control parameters to the network input.

show abstract

A configurable nonlinear operation unit for neural network accelerator

Cited by 2 publications

References 8 publications

Neural network modeling and dynamic behavior prediction of nonlinear dynamic systems

Neural network modeling and dynamic behavior prediction of nonlinear dynamic systems

Neural Network Modeling and Dynamic Behavior Prediction of Nonlinear Dynamic Systems

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