Development of the long-term forecast system based on the neural network of the potato late blight in Liangshan Prefecture

2010 ◽  
Author(s):  
Guozhao Peng ◽  
Qing Luo
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Potato Late Blight ◽  
Forecast System ◽  
Term Forecast ◽  
The Neural Network

Monitoring and Prediction of Air Quality using Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 47
Author(s):  
Shwetal Raipure
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Pollution Index ◽  
Learning Technologies ◽  
Short Term ◽  
Initial Information ◽  
Term Forecast ◽  
The Neural Network

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are  several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network  can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is  a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the  neural network model has proposed.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

EEG Data Mining Using PCA

2011 ◽  
pp. 161-180 ◽  
Author(s):  
Lenka Lhotská ◽  
Vladimír Krajca ◽  
Jitka Mohylová ◽  
Svojmil Petránek ◽  
Václav Gerla
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Principal Components ◽  
The Neural Network ◽  
Eeg Data ◽  
Eigen Decomposition ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike ◽  
Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

scholarly journals Using neural networks to describe tracer correlations

2004 ◽  
Vol 4 (1) ◽  
pp. 143-146 ◽  
Author(s):  
D. J. Lary ◽  
M. D. Müller ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Models ◽  
The Neural Network ◽  
Pressure Time ◽  
Fortran Code ◽  
Time Of Year ◽  
Hidden Layer ◽  
And Training

Abstract. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE) which has continuously observed CH4  (but not N2O) from 1991 till the present. The neural network Fortran code used is available for download.

THE PROBLEM OF MODELLING A TRUNK AIR ROUTE NETWORK

Aviation ◽  
2013 ◽  
Vol 17 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tatiana Tseytlina ◽  
Victor Balashov ◽  
Andrey Smirnov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Neural Network ◽  
Development Environment ◽  
Route Network ◽  
Information Models ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Neural Network Toolbox

In this work we developed a fuzzy neural network-based model of the conditions for the existence of air routes, i.e. the rules underlying the emergence, existence and elimination of air routes (direct links between cities). The model belongs to the class of information models: the existence or non-existence of an air route is considered dependent on a complex of parameters. These parameters characterise the transport link, as well as the generational and target capabilities of the connected cities. The model was constructed using genetic algorithm techniques and self-organising Kohonen maps (implemented by software features of the STATISTICA package), as well as software tools of the Fuzzy Logic Toolbox and the Neural Network Toolbox of the MatLab development environment. The model is used to forecast the development of the topology of the network. The forecast is a necessary component of long-term forecasts of demand in the aircraft market.

scholarly journals Perbandingan Metode ANN-PSO dan ANN-GA untuk Peningkatan Akurasi Prediksi Harga Emas Antam

2019 ◽  
Vol 10 (2) ◽  
pp. 101-106
Author(s):  
Nadia Annisa Maori ◽  
Keyword(s):  
Neural Network ◽  
Algorithm Optimization ◽  
Swarm Optimization ◽  
A Value ◽  
The Neural Network ◽  
The Us ◽  
Us Dollar ◽  
Artificial Neural Network Ann ◽  
Backpropagation Artificial Neural Network

Antam's gold price is more expensive than the price of gold used by more investors for long-term use. Sometimes the price of antam gold cannot be predicted at any time. Antam's rising gold prices were moved by many factors, sent in exchange rates of US dollars (USD). If the exchange rate of the US dollar (USD) decreases, the price of gold will rise and vice versa, if the value of the US dollar (USD) strengthens, the price of gold will increase. This condition makes it difficult for investors to predict the price of gold in the future. Backpropagation Artificial Neural Network (ANN) is known as one of the good methods in predicting. In this study an evaluation of the results of the price of gold using ANN with the help of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) optimization. PSO has many similarities with GA, which is an algorithm adopted from the process of supporting humans. The results of the study prove that PSO Optimization is able to provide an increase in optimizing the weights on the Neural Network by producing the best RMSE value, which is equal to 0.026, while GA optimization only produces a value of 0.09.

scholarly journals A Promising Wavelet Decomposition –NNARX Model to Predict Flood

2020 ◽  
Vol 11 (2) ◽  
pp. 89-99
Author(s):  
Mohd Azrol Syafiee Anuar ◽  
Ribhan Zafira Abdul Rahman ◽  
Azura Che Soh ◽  
Samsul Bahari Mohd Noor ◽  
Zed Diyana Zulkafli
Keyword(s):  
Neural Network ◽  
Flow Rate ◽  
Wavelet Decomposition ◽  
Performance Testing ◽  
Original Data ◽  
Flood Prediction ◽  
The Neural Network ◽  
Major Disaster ◽  
A New Technique

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2.

Long-Term Power Load Forecast Based on Improved BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 848-853
Author(s):  
Shuang Chen Li ◽  
Di Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Setup Time ◽  
Time Sequence ◽  
Bp Algorithm ◽  
Load Forecast ◽  
The Neural Network ◽  
Power Load

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

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