scholarly journals Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
M. Madhiarasan ◽  
Mohamed Louzazni ◽  
Partha Pratim Roy
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Solar Irradiance ◽  
Meteorological Parameters ◽  
Weather Condition ◽  
Forecasting Model ◽  
Generalization Ability ◽  
Forecasting Models

To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

scholarly journals Detection of Lung Cancer using Multilayer Perceptron Neural Network

2017 ◽  
Vol 1 (4) ◽  
pp. 109
Author(s):  
Farzad Mirzakhani
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Predictive Value ◽  
Artificial Neural Network Model

Introduction: Lung cancer is the most common cancer in terms of prevalence and mortality. The cancer can be detected once it is reached to a stage that is visible in the CT imaging. Eighty six percent of the patients with lung cancer because they are late understand their disease, surgery has little effect on their improvement. Therefore, the existence of an intelligent system that can detect lung cancer in the early stages is necessary. Methods: In this study, a lung cancer dataset of UCI database was used. This dataset consists of 32 samples, 57 variables and 3 classes (each class including 10, 9 and 13 samples). The data were normalized within the range 0 to 1. Then, to increase the detection speed, the dimensions of the data were reduced by using the Principal Components Analysis (PCA). Then, using a multilayer perceptron neural network, a model for classification and prediction of lung cancer was developed. Finally, the performance of the model was measured using sensitivity, specificity, positive predictive value and negative predictive value. It should be noted that all analyzes were done using Weka software. Results: After developing and evaluating an artificial neural network model, the developed model had a sensitivity of 66.7%, a 98.5% specificity, a positive predictive value of 75%, and a negative predictive value of 97.7%. Conclusion: In intelligent diagnostic systems, in addition to high accuracy of diagnosis, the speed of diagnosis and decision making is also important. Therefore, researchers increased the speed of the prediction model by reducing 57 variables to 8 variables using PCA. Also, the high sensitivity and high specificity of developed model demonstrates high power of artificial neural network model in detecting lung cancer.

Research on the GPS Elevation Fitting with RBF Neural Network Model Considering Effects of Sample Data Preprocessing

2014 ◽  
Vol 568-570 ◽  
pp. 817-821 ◽  
Author(s):  
Li Long Liu ◽  
Jun Yu Li ◽  
Chen Hui Cai ◽  
Guo Biao Lin
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Data Preprocessing ◽  
Generalization Ability ◽  
Preprocessing Method ◽  
Sample Data

RBF neural network and three kinds of preprocessing methods are introduced, and this paper used these preprocessing methods combined with RBF neural network and strict RBF neural network to perform elevation fitting. Comparing and analyzing the fitting results, the results show that preprocessing methods can affect elevation fitting results. Centralized preprocessing data maximum improves RBF neural network elevation fitting precision, and it also let RBF neural network have stronger generalization ability. Normalization preprocessing methods are not necessarily optimal. It is essential for us to choose preprocessing method to fit the elevation.

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