Optimizing the Weights of Neural Networks Based on Antibody Clonal Simulated Annealing Algorithm

2004 ◽  
pp. 299-304
Author(s):  
Xiaoyi Jin ◽  
Haifeng Du ◽  
Wuhong He ◽  
Licheng Jiao
Keyword(s):  
Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Annealing Algorithm

Simulation of the Mean-Zero-Up-Crossing Wave Period Using Artificial Neural Networks Trained With Simulated Annealing Algorithm

2006 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
F. Aryana ◽  
S. F. Yasseri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
North East ◽  
The North ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Annealing Algorithm ◽  
Artificial Neural

The aim of this work is to simulate the 3-houly mean zero-up-crossing wave periods (Tzs) of the sea-states of a future period for a location in the North East Pacific. Seven multi-layer artificial neural networks (ANNs) were trained with simulated annealing algorithm. The output of each ANN was used for estimating each of the 7 parameters of a new distribution, described in Appendix A, called hepta-parameter spline proposed for the conditional distribution of the Tz given some significant wave heights and mean zero-up-crossing wave periods. After estimating the parameters of the conditional distributions, the Tzs have been forecasted from the hepta-parameter spline distributions corresponding to the Tzs of the period.

scholarly journals Some Remarks on Replicated Simulated Annealing

2021 ◽  
Vol 182 (3) ◽  
Author(s):  
Vicent Gripon ◽  
Matthias Löwe ◽  
Franck Vermet
Keyword(s):  
Neural Networks ◽  
Simulated Annealing ◽  
Statistical Physics ◽  
Simulated Annealing Algorithm ◽  
Real Data ◽  
Data Bases ◽  
Physics Literature ◽  
Annealing Algorithm

AbstractRecently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statistical physics literature. Among other points, they claim their method is able to find robust configurations. In this paper, we analyze this so called “replicated simulated annealing” algorithm. In particular, we give criteria to guarantee its convergence, and study when it successfully samples from configurations. We also perform experiments using synthetic and real data bases.

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