Weather Monitoring System AIoT Based for Oil Palm Plantation Using Recurrent Neural Network Algorithm

2021 ◽  
Author(s):  
Prasetyo Mimboro ◽  
Ford Lumban Gaol ◽  
Harco Lesie Hendric Spits Warnars ◽  
Banefano Soewito
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Recurrent Neural Network ◽  
Oil Palm ◽  
Oil Palm Plantation ◽  
Network Algorithm ◽  
Neural Network Algorithm

Software Design and Implementation of Large-Scale Intelligent Traffic Monitoring System

2014 ◽  
Vol 644-650 ◽  
pp. 1351-1354
Author(s):  
Jun Ye Wang
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Large Scale ◽  
Rbf Neural Network ◽  
Traffic Monitoring ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Traffic State ◽  
Network Algorithm ◽  
Neural Network Algorithm

The design method of large-scale intelligent traffic monitoring system is studied. Traffic monitoring methods have become the core problem of intelligent transportation research field. To this end, this paper proposes an intelligent traffic monitoring method based on clustering RBF neural network algorithm. Fourier coefficient normalization method is used to extract the feature of traffic state, to be as the basis for intelligent traffic monitoring. Using clustering RBF neural network algorithm identify the traffic state effectively, thus to complete the state recognition of intelligent traffic monitoring. Experimental results show that the proposed algorithm performed in intelligent traffic monitoring, can greatly improve the accuracy of monitoring.

A STUDY OF TRACK FINDING WITH A NEURAL NETWORK ALGORITHM

1992 ◽  
Vol 03 (supp01) ◽  
pp. 303-308
Author(s):  
Giuseppe Barbagli ◽  
Guido Castellini ◽  
Gregorio Landi ◽  
Stefano Vettori
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Hardware Implementation ◽  
Initial Conditions ◽  
Hopfield Model ◽  
Network Algorithm ◽  
Random Initial Conditions ◽  
Neural Network Algorithm

We have investigated the problem of track finding with a recurrent neural network algorithm based on the Hopfield model and considered the possibility of a hardware implementation with DSP’s. Starting from a set of signal points we define track segments and set a cut on the length to keep the size of the network reasonable. Those segments surviving the cut are associated to neurons. A geometric coupling of neighbouring segments is used to select smooth combinations of them. Given random initial conditions the network converges to a solution. The method may be applied to a variety of curves.

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