An Environmental Cost Value Model Based on Dynamic Neural Network Prediction

Wavelet Neural Network (WNN) is a new form of neural network combined with the wavelet theory and artificial neural network. The wavelet neural network model based on Morlet wavelet and the corresponding learning algorithm were studied in this paper. And through learning the wavelet neural network model is applied to all kinds of engineering examples, it proved that the wavelet neural network prediction model which has a more flexible and efficient function approximation ability and strong fault tolerance, and with high predicting precision.

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A fuzzy neural network prediction model based on manifold learning to reduce dimensions for typhoon intensity

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea.2013.6566432 ◽

2013 ◽

Author(s):

Jin Long ◽

Shi Xvming ◽

Huang Xiaoyan ◽

Huang Ying

Keyword(s):

Neural Network ◽

Prediction Model ◽

Manifold Learning ◽

Fuzzy Neural Network ◽

Model Based ◽

Typhoon Intensity ◽

Fuzzy Neural ◽

Neural Network Prediction ◽

Network Prediction

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Sales Forecasting Model Based on BP Neural Network Optimized by Improved Immune Genetic Algorithm

10.21203/rs.3.rs-598556/v1 ◽

2021 ◽

Author(s):

Kunyu Cao ◽

Yongdang Chen ◽

Xinxin Song ◽

Shan Liu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Bp Neural Network ◽

Prediction Models ◽

Forecasting Model ◽

Antibody Concentration ◽

Immune Genetic Algorithm ◽

Model Based ◽

Neural Network Prediction ◽

Network Prediction

Abstract A new sales forecasting model based on an Improved Immune Genetic Algorithm (IIGA), IIGA that optimizes the BPNN (IIGA-BP) has been proposed. The IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operator and mutation operator, which effectively improved the convergence ability and optimization anility of IIGA. And IIGA can optimize the BPNN’s initial weights and threshold and improve the randomness of network parameters as well as the drawbacks that lead to output instability of BPNN and easiness into local minimum value. It taking the past records of sales figures of a certain steel enterprise as an example, utilizing the Matlab to construct the BP neural network, Immune Genetic Algorithm that optimizes the BPNN (IGA-BP), IGA-BP neural network, and IIGA-BP neural network prediction models for simulation and comparative analysis. The experiment demonstrates that IIGA-BP neural network prediction model possessing a higher prediction accuracy and more stable prediction effects.

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Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190429161911 ◽

2020 ◽

Vol 13 (4) ◽

pp. 657-671

Author(s):

Wei Jiang ◽

Hongmei Xu ◽

Elnaz Akbari ◽

Jiang Wen ◽

Shuang Liu ◽

...

Keyword(s):

Neural Network ◽

Moisture Content ◽

Prediction Model ◽

Bp Neural Network ◽

Prediction Method ◽

Principal Component ◽

Spectral Difference ◽

Model Based ◽

Neural Network Prediction ◽

Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

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