scholarly journals Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

2012 ◽  
Vol 4 (11) ◽  
pp. 3571-3595 ◽  
Author(s):  
Gonzalo Pajares ◽  
Carlos López-Martínez ◽  
F. Sánchez-Lladó ◽  
Íñigo Molina
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Hopfield Neural Network ◽  
Optimization Approach ◽  
Polarimetric Sar ◽  
Neural Network Optimization ◽  
Sar Data

scholarly journals CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Katmoko Ari Sambodo ◽  
Aniati Murni ◽  
Mahdi Kartasasmita ◽  
Mahdi Kartasasmita
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Network Architecture ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
Polarimetric Sar ◽  
Decomposition Model ◽  
Combined Features ◽  
Sar Data

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Keywords: Polarimetric-SAR, scattering model, freeman decomposition, Cloude decomposition, texture analysis, feature extraction, classification, neural networks.

Optimal Power Control of Smart Grid Based on Neural Network Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 626-632
Author(s):  
Li Yan Zhang ◽  
Shu Hai Quan
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Smart Grid ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Approach ◽  
Neural Network Optimization ◽  
Control Approach ◽  
Optimal Power ◽  
Control Layer

This paper presents a neural network based optimal control approach for the power allocation problem of smart grid. Firstly the optimal control framework which consists of center control layer, area control layer and generator control layer is presented. In order to reduce computation time of optimal control, the neural network optimization method and schematic diagram is proposed. Simulation is implemented in a modified IEEE30-bus power system comprising 6 conventional generators and a wind plant. Simulation results demonstrate that the proposed neural network optimization approach is satisfied the demand of computation time and the optimal power controller can allocate the power into loads reasonably and cope with the fluctuation of the load and renewable energy source’s output power.

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