Artificial Neural Network for Cooperative Distributed Environments

Paletta, Mauricio

doi:10.5772/15984

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As an information processing science, ANN has been developed for many years. After experiencing the tortuous road of rising, climax, depression, and revival, it has progressed steadily; see [4,10,21]. However, with the development of ANN, researchers discovered that it does not consider the correlation between the data in practical applications, and the output of the network is only related to the input at current [19].…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network with $L_{1/2}$ regularization for regression and multiclass classification problems

2022

J. Nonlinear Funct. Anal.

View full text Add to dashboard Cite

Recurrent neural network (RNN) is introduced to solve the dynamic system problem. In this paper, a new RNN of the gradient method with L 1/2 regularization learned sequential behavior is presented. L 1/2 regularization can drive redundant weight vectors of nodes to zero efficiently. The usual L 1/2 regularization involves a non-smooth absolute value function, which causes the oscillation of the norm of gradient and the error function in the numerical computation. However, by smoothing techniques, those drawbacks can be well addressed. Simulation results of regression and multiclass classification problems demonstrate that our algorithm has better performance than three other algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Recurrent neural network with $L_{1/2}$ regularization for regression and multiclass classification problems

2022

J. Nonlinear Funct. Anal.

View full text Add to dashboard Cite

show abstract

Comparative analysis of accuracy in image forensics for authentication using novel convolutional neural networks with artificial neural networks algorithm

Reddy,

Nithya

2023

INTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings

View full text Add to dashboard Cite

Feature Extraction and Recognition Based on the Biological Analysis

Zhao

Wang

2014

AMM

View full text Add to dashboard Cite

In this paper the extracted features including rectangularity,elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter,the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

show abstract

Artificial Neural Network for Cooperative Distributed Environments

Cited by 3 publications

References 36 publications

Recurrent neural network with $L_{1/2}$ regularization for regression and multiclass classification problems

Recurrent neural network with $L_{1/2}$ regularization for regression and multiclass classification problems

Comparative analysis of accuracy in image forensics for authentication using novel convolutional neural networks with artificial neural networks algorithm

Feature Extraction and Recognition Based on the Biological Analysis

Contact Info

Product

Resources

About