scholarly journals Multilayer perceptron for face recognition

2017 ◽  
Vol 58 ◽  
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Multilayer Perceptron ◽  
Gradient Descent ◽  
Optimization Algorithms ◽  
Effective Model ◽  
Activation Functions ◽  
Facial Landmarks ◽  
Network Parameters ◽  
Euclidean Distances

In this paper, an algorithm is proposed which uses facial landmarks to calculate normalized Euclidean distances between different facial parts and performs faces recognition by using Multilayer Perceptron. In order to determine the most effective model, different neural network parameters have been changed in an experimental way, such as hidden layers and the number of neurons, gradient descent optimization algorithms, error and activation functions, and different sets of distances.

scholarly journals Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1281
Author(s):  
Je-Chian Chen ◽  
Yu-Min Wang
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Aerial Survey ◽  
Activation Functions ◽  
Shoreline Changes ◽  
Extensive Evaluation ◽  
Aerial Vehicle ◽  
Sine Functions ◽  
Mlp Model

The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as predictors, while the unmanned aerial vehicle (UAV) surveyed data of 2019 served as the respondent. The MLP was configured using five different activation functions with the aim of evaluating their significance. These functions were Identity, Tahn, Logistic, Exponential, and Sine Functions. The results have shown that the performance of an MLP model may be affected by the choice of an activation function. Logistic and the Tahn activation functions outperformed the other models, with Logistic performing best in three beaches and Tahn having the rest. These findings suggest that the application of machine learning to shoreline changes should be accompanied by an extensive evaluation of the different activation functions.

scholarly journals A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index

2021 ◽  
Vol 33 (3) ◽  
pp. 373-385
Author(s):  
Duy Tran Quang ◽  
Sang Hoon Bae
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Congestion ◽  
Gradient Descent ◽  
Optimization Algorithms ◽  
Deep Convolutional Neural Network ◽  
Short Term ◽  
Probe Vehicles ◽  
Proposed Model ◽  
Congestion Index

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

THE EFFECT OF NETWORK PARAMETERS ON PI-SIGMA NEURAL NETWORK FOR TEMPERATURE FORECASTING

2012 ◽  
Vol 09 ◽  
pp. 440-447 ◽  
Author(s):  
NOOR AIDA HUSAINI ◽  
ROZAIDA GHAZALI ◽  
NAZRI MOHD NAWI ◽  
LOKMAN HAKIM ISMAIL
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Meteorological Parameters ◽  
Suburban Area ◽  
Forecasting Model ◽  
Backpropagation Algorithm ◽  
Network Parameters ◽  
Temperature Forecasting ◽  
One Step ◽  
The Common

In this paper, we present the effect of network parameters to forecast temperature of a suburban area in Batu Pahat, Johor. The common ways of predicting the temperature using Neural Network has been applied for most meteorological parameters. However, researchers frequently neglected the network parameters which might affect the Neural Network's performance. Therefore, this study tends to explore the effect of network parameters by using Pi Sigma Neural Network (PSNN) with backpropagation algorithm. The network's performance is evaluated using the historical dataset of temperature in Batu Pahat for one step-ahead and benchmarked against Multilayer Perceptron (MLP) for comparison. We found out that, network parameters have significantly affected the performance of PSNN for temperature forecasting. Towards the end of this paper, we concluded the best forecasting model to predict the temperature based on the comparison of our study.

scholarly journals Computations, optimization and tuning of deep feedforward neural networks

2019 ◽  
Author(s):  
Md. Shoaibur Rahman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Algorithms ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Cost Functions ◽  
Activation Functions ◽  
Parameters Tuning

AbstractThis article presents an overview of the generalized formulations of the computations, optimization, and tuning of a deep feedforward neural network. A small network has been used to systematically explain the computing steps, which were then used to establish the generalized forms of the computations in forward and backward propagations for larger networks. Additionally, some of the commonly used cost functions, activation functions, optimization algorithms, and hyper-parameters tuning approaches have been discussed.

scholarly journals Stacked Heterogeneous Neural Networks for Time Series Forecasting

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Florin Leon ◽  
Mihai Horia Zaharia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Case Studies ◽  
Multilayer Perceptron ◽  
Neural Model ◽  
Activation Function ◽  
Time Series Forecasting ◽  
The Other ◽  
Activation Functions

A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

scholarly journals Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

2019 ◽  
Vol 9 (18) ◽  
pp. 3664 ◽  
Author(s):  
Deying Li ◽  
Faming Huang ◽  
Liangxuan Yan ◽  
Zhongshan Cao ◽  
Jiawu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Statistical Models ◽  
Landslide Susceptibility ◽  
Gradient Descent ◽  
Information Value ◽  
Descent Algorithm ◽  
Landslide Occurrence ◽  
Gradient Descent Algorithm ◽  
Mlp Model

Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

scholarly journals Universal activation function for machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brosnan Yuen ◽  
Minh Tu Hoang ◽  
Xiaodai Dong ◽  
Tao Lu
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Signal To Noise Ratio ◽  
Activation Function ◽  
Optimal Performance ◽  
Mean Square ◽  
Activation Functions ◽  
Signal To Noise ◽  
Identity Function ◽  
Graph Neural Networks

AbstractThis article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters. For the CIFAR-10 classification using the VGG-8 neural network, the UAF converges to the Mish like activation function, which has near optimal performance $$F_{1}=0.902\pm 0.004$$ F 1 = 0.902 ± 0.004 when compared to other activation functions. In the graph convolutional neural network on the CORA dataset, the UAF evolves to the identity function and obtains $$F_1=0.835\pm 0.008$$ F 1 = 0.835 ± 0.008 . For the quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR) environments, the UAF converges to the identity function, which has near optimal root mean square error of $$0.489\pm 0.003~\mu {\mathrm{M}}$$ 0.489 ± 0.003 μ M . In the ZINC molecular solubility quantification using graph neural networks, the UAF morphs to a LeakyReLU/Sigmoid hybrid and achieves RMSE=$$0.47\pm 0.04$$ 0.47 ± 0.04 . For the BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in $${961\pm 193}$$ 961 ± 193 epochs with a brand new activation function, which gives the fastest convergence rate among the activation functions.

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