scholarly journals Semi-Supervised Minimum Error Entropy Principle with Distributed Method

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 968
Author(s):  
Baobin Wang ◽  
Ting Hu
Keyword(s):  
Gradient Descent ◽  
Mean Squared Error ◽  
Learning Ability ◽  
Minimum Error ◽  
Additional Information ◽  
Squared Error ◽  
Gradient Descent Algorithm ◽  
Entropy Principle ◽  
Non Gaussian ◽  
Distributed Method

The minimum error entropy principle (MEE) is an alternative of the classical least squares for its robustness to non-Gaussian noise. This paper studies the gradient descent algorithm for MEE with a semi-supervised approach and distributed method, and shows that using the additional information of unlabeled data can enhance the learning ability of the distributed MEE algorithm. Our result proves that the mean squared error of the distributed gradient descent MEE algorithm can be minimax optimal for regression if the number of local machines increases polynomially as the total datasize.

scholarly journals Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Mary Opokua Ansong ◽  
Jun Steed Huang ◽  
Mary Ann Yeboah ◽  
Han Dun ◽  
Hongxing Yao
Keyword(s):  
Hybrid Model ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Network Efficiency ◽  
Gaussian Type ◽  
Squared Error ◽  
The Neural Network ◽  
Radial Basis ◽  
Non Gaussian ◽  
Complexity Increase

Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models.

scholarly journals RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

Computers ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 59 ◽  
Author(s):  
Ayyaz-Ul-Haq Qureshi ◽  
Hadi Larijani ◽  
Nhamoinesu Mtetwa ◽  
Abbas Javed ◽  
Jawad Ahmad
Keyword(s):  
Intrusion Detection ◽  
Mean Squared Error ◽  
Detection System ◽  
Data Set ◽  
Bee Colony ◽  
Squared Error ◽  
Random Neural Network ◽  
Prime Concern ◽  
Gradient Descent Algorithm ◽  
Memory Resources

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

scholarly journals Deep Learned Quantization-Based Codec for 3D Airborne LiDAR Point Cloud Images

2021 ◽  
Vol 8 ◽  
Author(s):  
A. Christoper Tamilmathi ◽  
P. L. Chithra
Keyword(s):  
Point Cloud ◽  
Gradient Descent ◽  
Mean Squared Error ◽  
Airborne Lidar ◽  
Stochastic Gradient Descent ◽  
Squared Error ◽  
Proposed Model ◽  
Optimization Function ◽  
Execution Speed

This paper introduces a novel deep learned quantization-based coding for 3D Airborne LiDAR (Light detection and ranging) point cloud (pcd) image (DLQCPCD). The raw pcd signals are sampled and transformed by applying the Nyquist signal sampling and Min-max signal transformation techniques, respectively for improving the efficiency of the training process. Then, the transformed signals are feed into the deep learned quantization module for compressing the data. To the best of our knowledge, this proposed DLQCPCD is the first deep learning-based model for 3D airborne LiDAR pcd compression. The functions of Mean Squared Error and Stochastic Gradient Descent optimization function enhance the quality of the decompressed image by 67.01 percent on average, compared to other functions. The model’s efficiency has been validated with established well-known compression techniques such as the 7-Zip, WinRAR, and tensor tucker decomposition algorithm on the three inconsistent airborne datasets. The experimental results show that the proposed model compresses every pcd image into constant 16 Number of Neurons of data and decompresses the image with approximately 160 dB of PSNR value, 174.46 s execution time with 0.6 s execution speed per instruction, and proved that it outperforms the other existing algorithms regarding space and time.

CMA Blind Multiuser Detector Based on FLOS in Impulse Noise

2011 ◽  
Vol 403-408 ◽  
pp. 187-190
Author(s):  
Xu Xiu Zhang ◽  
Chang An Huang
Keyword(s):  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Constant Modulus Algorithm ◽  
Multiuser Detector ◽  
Gradient Descent Algorithm ◽  
Non Gaussian ◽  
Theoretical Analyses ◽  
Special Case

Blind multiuser detector can suppress the MAI(multiple address interference) effectively. Gaussian channel noise is assumed in the traditional methods, but the non-Gaussian channel noise is more realistic. This paper proposes a new CMA(Constant Modulus Algorithm) criterion employing FLOS(fractional lower-order statistic). Theoretical analyses and the computer simulations indicate that the associated FLOS-CMA blind MUD(MultiUser Detection) method,based on a stochastic gradient descent algorithm has a good performance in BER(bit error rate). The traditional MUD algorithm is the special case of this algorithm.

scholarly journals Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation ( Studi kasus : SMP Negeri 1 Salapian)

2021 ◽  
Vol 1 (2) ◽  
pp. 54-58
Author(s):  
Ninta Liana Br Sitepu
Keyword(s):  
Human Brain ◽  
Gradient Descent ◽  
Mean Squared Error ◽  
Descent Method ◽  
Training Data ◽  
Gradient Descent Method ◽  
A Value ◽  
Squared Error ◽  
Sample Data ◽  
The Mean

Backpropagationcial neural networks are one of the artificial representations of the human brain that are always trying to stimulate the learning process of the human brain. Backpropagation is a gradient descent method to minimize the squared of the output error. Backprorpagation works through an iterative process using a set of sample data (training data), comparing the predicted value of the network with each sample data. In each process, the weight of the relation in the network is modified to minimize the Mean Squared Error value between the predicted value from the network and the actual value. The purpose of this thesis is to be able to help teachers at SMP Negeri 1 Salakaran to predict the value of student learning. In the calculation using the maximum epouch = 10000, the target error is 0.01, and the learning rate is 0.3, then there is a calculation result where the need ratio A has a value of 0.7517, which means that the value has decreased and D has a value of 0.9202 which means that this value has increased..

scholarly journals Forecasting PM10 in the Bay of Algeciras Based on Regression Models

2019 ◽  
Vol 11 (4) ◽  
pp. 968 ◽  
Author(s):  
José Palomares-Salas ◽  
Juan González-de-la-Rosa ◽  
Agustín Agüera-Pérez ◽  
José Sierra-Fernández ◽  
Olivia Florencias-Oliveros
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Average Concentration ◽  
Improve Performance ◽  
Urban Centers ◽  
Experimental Procedure ◽  
Additional Information ◽  
Squared Error ◽  
The Mean ◽  
Quality Indexes

Different forecasting methodologies, classified into parametric and nonparametric, were studied in order to predict the average concentration of P M 10 over the course of 24 h. The comparison of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient, the index of agreement, the mean absolute error, and the root mean squared error). The proposed experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras (Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained with the reference models through the introduction of low-quality measurements as exogenous information. This proves that it is possible to improve performance by using additional information from the existing nonlinear relationships between the concentration of the pollutants and the meteorological variables.

Development Of Water Quality Prediction Model For Narmada River Using Artificial Neural Networks

2021 ◽  
Author(s):  
Shubham Lakhera ◽  
Sunayana Chandra ◽  
Dal Chand Rahi
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Gradient Descent ◽  
Mean Squared Error ◽  
Water Quality Assessment ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Squared Error ◽  
Narmada River ◽  
Hidden Layer

Abstract The lack of a universal system for analysis, prediction, and storage of water quality and condition of rivers in Madhya Pradesh has led to uneven policy-making and poor management ultimately posing issues in health, irrigation and keep increasing pollution in rivers. This study is a part of developing a central system for river water quality assessment and prediction. The conventional method of water quality assessment is based on the calculation of the water quality index which can be very complex and time-consuming. This paper aims to develop a water quality prediction model with the help of an Artificial Neural Network (ANN) for predicting the water quality of the Narmada River using two machine learning algorithms Levenberg and Gradient Descent and the results were compared. This research uses the surface water historical data of years 2018, 2019 of the river Narmada with monthly time intervals. Data is obtained from the Central Pollution Control Board resource called Narmada Automatic Sampling Collection Stations System. For training the network 10 water quality parameters including, DO, BOD, Turbidity, pH, etc. After training the networks were accessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Coefficient of Correlation (R) out of which 2 best performing networks with 7 ( Training R = 0.80083, Testing R = 0.5767) and 19 (Training R = 0.6594, Testing R = 0.7424) Neurons in the hidden layer, were selected from Levenberg algorithm and, 5 (Training R = 0.7670, Testing R = 0.8123) & 17 (Training R = 0.8631, Testing R = 0.8981) Neurons in the hidden layer were selected from Gradient descent algorithm. This simplifies the calculation of WQI take care if any sampling station is out of service and data is not available for some reason. Further, the aim is to refine the prediction location-wise to be able to make a better decision when & where to implement the measures to reduce the pollution or the knowledge level of treatment required to make the water fit for use beforehand. This would be helpful in the treatment of water for use in Domestic or Irrigation Purposes.

scholarly journals Road Traffic Monitoring from UAV Images Using Deep Learning Networks

2021 ◽  
Vol 13 (20) ◽  
pp. 4027
Author(s):  
Sungwoo Byun ◽  
In-Kyoung Shin ◽  
Jucheol Moon ◽  
Jiyoung Kang ◽  
Sang-Il Choi
Keyword(s):  
Mean Squared Error ◽  
Road Traffic ◽  
Recall Rate ◽  
Traffic Monitoring ◽  
Learning Networks ◽  
Additional Information ◽  
Squared Error ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Precision Rate

In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV). The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating the speeds of vehicles on the roads. Our method can automatically measure the speed of the vehicles from the only videos recorded using UAV without additional information in both directions on the roads simultaneously. In our experiments, we evaluate the performance of the proposed method with the visual data at four different locations. The proposed method shows 97.6% recall rate and 94.7% precision rate in detecting vehicles, and it shows error (root mean squared error) of 5.27 km/h in estimating the speeds of vehicles.

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