Groundwater Level Prediction of Shahrood Plain using RBF Neural Networks

Abstract In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, revealing a spatial pattern of stronger decreases especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

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Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA

Neural Computing and Applications ◽

10.1007/s00521-021-05706-3 ◽

2021 ◽

Author(s):

Volodymyr Shymkovych ◽

Sergii Telenyk ◽

Petro Kravets

Keyword(s):

Neural Networks ◽

Hardware Implementation ◽

Gaussian Function ◽

Activation Function ◽

Rbf Neural Networks ◽

Activation Functions ◽

Rbf Network ◽

Combination Scheme ◽

Radial Basis ◽

Hidden Layer

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

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Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

Measurement and Control ◽

10.1177/0020294020983383 ◽

2021 ◽

Vol 54 (1-2) ◽

pp. 102-115

Author(s):

Wenhui Si ◽

Lingyan Zhao ◽

Jianping Wei ◽

Zhiguang Guan

Keyword(s):

Neural Networks ◽

Asymptotic Stability ◽

Control Method ◽

Joint Space ◽

Robot Kinematics ◽

Rbf Neural Networks ◽

Task Space ◽

Actuator Dynamics ◽

Rigid Link ◽

On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

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