A backpropagation neural network improved by a genetic algorithm for predicting the mean radiant temperature around buildings within the long-term period of the near future

2021 ◽  
Author(s):  
Yuquan Xie ◽  
Yasuyuki Ishida ◽  
Jialong Hu ◽  
Akashi Mochida
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Mean Radiant Temperature ◽  
Backpropagation Neural Network ◽  
The Mean ◽  
Radiant Temperature ◽  
Near Future

Research on the BPNN in the Prediction of PMV

2010 ◽  
Vol 29-32 ◽  
pp. 2804-2808 ◽  
Author(s):  
Jian Yao ◽  
Jin Xu
Keyword(s):  
Neural Network ◽  
Iterative Process ◽  
Absolute Error ◽  
Mean Radiant Temperature ◽  
Backpropagation Neural Network ◽  
Air Velocity ◽  
The Neural Network ◽  
Radiant Temperature ◽  
Prediction Approach ◽  
Bpnn Model

In view of the problem that it is difficult to calculate the Fanger’s PMV equation due to its complicated iterative process, a backpropagation neural network (BPNN) model was built to predict PMV. Air temperature, relative humidity, mean radiant temperature, air velocity, metabolic rate and clothing index were used as the input of neural network and PMV output as the output of the neural network. The results show that this prediction approach is very effective and has higher accuracy absolute error below 5%. As a conclusion, this study has a real significance, because it gives a new method with reliability and accuracy in the prediction of PMV.

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.

scholarly journals Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Lu ◽  
Menghan Liu ◽  
Jie Zhou ◽  
Zhigang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Poor Performance ◽  
Data Sets ◽  
Backpropagation Neural Network ◽  
External Threats ◽  
Kdd Cup 99

Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.

A Genetic Algorithm to Improve a Neural Network to Predict a Patient’s Response to Warfarin

1993 ◽  
Vol 32 (01) ◽  
pp. 55-58 ◽  
Author(s):  
M. N. Narayanan ◽  
S. B. Lucas
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Predictive Ability ◽  
Predictor Variables ◽  
Optimal Subset ◽  
The Neural Network ◽  
International Normalised Ratio ◽  
The Mean ◽  
Paired T Test

Abstract:The ability of neural networks to predict the international normalised ratio (INR) for patients treated with Warfarin was investigated. Neural networks were obtained by using all the predictor variables in the neural network, or by using a genetic algorithm to select an optimal subset of predictor variables in a neural network. The use of a genetic algorithm gave a marked and significant improvement in the prediction of the INR in two of the three cases investigated. The mean error in these cases, typically, reduced from 1.02 ± 0.29 to 0.28 ± 0.25 (paired t-test, t = −4.71, p <0.001, n = 30). The use of a genetic algorithm with Warfarin data offers a significant enhancement of the predictive ability of a neural network with Warfarin data, identifies significant predictor variables, reduces the size of the neural network and thus the speed at which the reduced network can be trained, and reduces the sensitivity of a network to over-training.

scholarly journals The most problematic variable in the course of human-biometeorological comfort assessment — the mean radiant temperature

2011 ◽  
Vol 3 (1) ◽  
Author(s):  
Noémi Kántor ◽  
János Unger
Keyword(s):  
Thermal Comfort ◽  
Short Wave ◽  
Urban Environments ◽  
Outdoor Thermal Comfort ◽  
Radiation Environment ◽  
Wave Radiation ◽  
Mean Radiant Temperature ◽  
Radiation Fluxes ◽  
The Mean ◽  
Radiant Temperature

AbstractThis paper gives a review on the topic of the mean radiant temperature Tmrt, the most important parameter influencing outdoor thermal comfort during sunny conditions. Tmrt summarizes all short wave and long wave radiation fluxes reaching the human body, which can be very complex (variable in spatial and also in temporal manner) in urban settings. Thermal comfort researchers and urban planners need easy and sound methodological approaches to assess Tmrt. After the basics of the Tmrt calculation some of the methods suitable for obtaining Tmrt also in urban environments will be presented.. Two of the discussed methods are based on instruments which measure the radiation fluxes integral (globe thermometer, pyranometer-pyrgeometer combination), and three of the methods are based on modelling the radiation environment with PC software (RayMan, ENVI-met and SOLWEIG).

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