Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Laabid, Zineb; Moumen, Aziz; Mansouri, Khalifa; Siadat, Ali

doi:10.11591/ijai.v12.i1.pp155-161

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Tere are many types of activation functions for the BP neural network prediction, each with its own advantages and disadvantages. In this paper, we choose the commonly used tansig function and purelin function as the hidden layer neuron and output layer neuron activation functions, respectively, whose expressions are [28] as follows:…”

Section: Bp Neural Networkmentioning

confidence: 99%

Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule

Fan,

Yang,

et al. 2023

Journal of Advanced Transportation

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In order to solve the problem that the railway passenger volume data are abnormal due to holidays and major events interfering with the prediction accuracy, the spline interpolation method is introduced to replace the abnormal passenger volume data. In addition, an improved particle swarm optimization (IPSO) is proposed to optimize the gradient difference acceleration law to combine and improve the predicted value and further improve the prediction accuracy of the railway passenger traffic. Finally, taking Beijing as the research object, the Holt exponential smoothing method and the BP neural network are selected to verify the effect of spline interpolation and IPSO-gradient difference acceleration law on prediction accuracy. The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. In comparison with the BP neural network, Holt exponential smoothing, simple averaging, and conventional redifference approaches, the IPSO-redifference acceleration method achieves a superior prediction performance, and the absolute values of the forecast error are reduced by 3.320%, 1.518%, 2.419%, and 0.602%.

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Section: Bp Neural Networkmentioning

confidence: 99%

Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule

Fan,

Yang,

et al. 2023

Journal of Advanced Transportation

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Model Reference‐Based Neural Controller for Transmission Line Inspection Robot

Karagöz,

Ekren,

Demir

et al. 2024

Journal of Field Robotics

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The regular inspection of the power transmission lines is essential for the uninterrupted transmission of electrical energy to demand points. This quickly requires actions with economically, efficiently, and safely. Therefore, the transmission line inspection robots are inevitable solution as an alternative to existing line inspection methods. This study present design and control of a transmission line inspection robot (I‐Robot). Since the I‐Robot exhibits nonlinear behavior and has multiple inputs and multiple outputs, a model reference‐based neural controller is determined to achieve nonlinear control. The robot design process consists of four stages which are kinematic modelling, dynamic modelling, actuator modelling and controller design. To meet inspection requirements, the conceptual design of the I‐Robot is performed, and the kinematic model are calculated in terms of the transformation matrices. According to the design requirements and system constraints, the dynamic model of the I‐Robot is created. To provide desired motions and trajectory tracking, the actuator models are determined. Then, the I‐Robot is prototyped. According to the dynamics of joint, robot and constraints, the system identification is performed to create reference model. During the system identification, the logged data are used the train the reference model. Finally, the desired trajectory for the driving cycles is created by manual excitation of the I‐Robot. During the manual excitation, the logged data are used to train the neural network (NN)‐based controller. Eventually, the I‐Robot is assessed under the test scenarios in term of the trajectory tracking performance as regression value and mean squared errors. According to the experiments, the neuron numbers and the training algorithm of the NN controller are determined. It was observed that the controller is quickly optimized with the adapting algorithm designed for the NN reference model. As a result, the performance of the model reference‐based neural controller was determined as 99%.

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Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

Mwiti,

Gitau,

Mbuge

2024

Computers and Electronics in Agriculture

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Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Cited by 3 publications

References 17 publications

Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule

Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule

Model Reference‐Based Neural Controller for Transmission Line Inspection Robot

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

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