The Improved Training Algorithm of Back Propagation Neural Network with Self-adaptive Learning Rate

2009 ◽  
Author(s):  
Yong Li ◽  
Yang Fu ◽  
Hui Li ◽  
Si-Wen Zhang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Training Algorithm ◽  
Adaptive Learning Rate ◽  
Self Adaptive

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

Longitudinal Control for AUV with Self-Adaptive Learning Law

2013 ◽  
Vol 819 ◽  
pp. 259-265
Author(s):  
Xiu Jun Sun ◽  
Yan Yang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Attitude Control ◽  
Autonomous Underwater Vehicle ◽  
Linear Equations ◽  
Back Propagation ◽  
Mathematic Model ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Self Adaptive

A mini AUV (Autonomous Underwater Vehicle) with cross shaped rudders and one single thruster is presented, which features high maneuverability due to the intelligent control algorithm. A single variable PID neural network controller is also proposed, which is utilized to maintain attitude for the vehicle. In order to testify feasibility of the control methodology, a spatial motion mathematic model is constructed and linear equations that indicate the relation between attitude angles of vehicle and deflection angles of rudders is deduced firstly. Subsequently, the neural network PID controller is developed according to the deduced equations and the attitude control simulation of the vehicle with this controller is conducted. Taking actual and desired attitude angles of the vehicle as input and deflection angles of the rudders as output, this controller performs self-adaptive update for 9 synaptic weights through back-propagation algorithm and employs the converged weights to calculate the appropriate deflection angle of each rudder.

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