A Smoothing Algorithm with Constant Learning Rate for Training Two Kinds of Fuzzy Neural Networks and Its Convergence

Li, Long; Zhang, Qiao; Long, Zuqiang

doi:10.1007/s11063-019-10135-4

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…• Initial Learn Rate [60]: the Learn Rate governs the rate at which the algorithm learns the neural network model parameters. At the beginning of the learning process, the model parameters are far from those that minimise the cost function [61], so it will be useful to have a high initial learning rate for a faster decrease.…”

Section: B Choice Of Hyperparameters To Be Optimizedmentioning

confidence: 99%

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

Marotta,

Strano,

Terzo

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle's sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.

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Section: B Choice Of Hyperparameters To Be Optimizedmentioning

confidence: 99%

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

Marotta,

Strano,

Terzo

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…According to the comparison of the classification error and convergence speed of the BP network composed of different hidden layer nodes, the BP network topology used in the classification experiment of the data set in the computer network is finally determined: 12 input layer nodes, 36 hidden layer nodes, and 12 output layer nodes [13]. Carrying on the final classification experiment with the selected network structure, the result is shown as in Figure 3.…”

Section: Neural Network and Classification Algorithmmentioning

confidence: 99%

Digital Computer Modulation Signal Classification Based on Neural Network

Wang

2022

Wireless Communications and Mobile Computing

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Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.

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Convergence of Batch Gradient Method Based on the Entropy Error Function for Feedforward Neural Networks

Xiong

Tong

2020

Neural Process Lett

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A Smoothing Algorithm with Constant Learning Rate for Training Two Kinds of Fuzzy Neural Networks and Its Convergence

Cited by 5 publications

References 28 publications

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

On the Prediction of the Sideslip Angle Using Dynamic Neural Networks

Digital Computer Modulation Signal Classification Based on Neural Network

Convergence of Batch Gradient Method Based on the Entropy Error Function for Feedforward Neural Networks

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