Application of fuzzy neural network to power system short-term load forecast

2010 ◽  
Author(s):  
Feng Han ◽  
Qing Zhang ◽  
Xu Zhang ◽  
Tingjiao Li
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Short Term ◽  
Load Forecast ◽  
Fuzzy Neural

Applying Artificial Intelligence to Short-Term Traffic Flow Forecasting

2012 ◽  
Vol 433-440 ◽  
pp. 5214-5217
Author(s):  
Hai Huang
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Optimization Algorithm ◽  
Fuzzy Neural Network ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fuzzy Neural ◽  
Low Efficiency

Short-term traffic flow forecasting has a high requirement for the responding time and accuracy of the forecasting method because the result is directly used for instant traffic inducing. Based on the introduction of the fuzzy neural network model for short-term traffic flow forecasting together with its detailed procedures, this paper adopt the particle swarm optimization algorithm to train the fuzzy neural network. Its global searching and optimization algorithm helps to overcome the shortcomings of the traditional fuzzy neural network, such as its low efficiency and “local optimum”. A case study is also given for the PSO algorithm to train the fuzzy neural network for traffic flow forecasting. The result shows that the average square error is 0.932 when the PSO algorithm is put to use for the network training, which is 3.926 when the PSO is not used. Thus result is more accurate and it requires less time for the training procedures. It proves this method is feasible and efficient.

scholarly journals Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin Zhang ◽  
Longhua Mu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Chaotic Oscillation ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

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