Optical acceptance function of modified compound parabolic concentrators with linear corrugated reflectors

1998 ◽  
Vol 37 (22) ◽  
pp. 5222 ◽  
Author(s):  
Mats Rönnelid ◽  
Björn Karlsson
Keyword(s):  
Acceptance Function

Shape of electron lines emitted by a fast particle in atomic collisions. Influence of the acceptance function

1980 ◽  
Vol 13 (2) ◽  
pp. 209-215 ◽  
Author(s):  
A Bordenave-Montesquieu ◽  
A Gleizes ◽  
P Benoit-Cattin ◽  
M Boudjema
Keyword(s):  
Fast Particle ◽  
Atomic Collisions ◽  
Acceptance Function

KNOWLEDGE-BASED SELF-ADAPTATION IN EVOLUTIONARY SEARCH

2000 ◽  
Vol 14 (01) ◽  
pp. 19-33 ◽  
Author(s):  
CHAN-JIN CHUNG ◽  
ROBERT G. REYNOLDS
Keyword(s):  
Fuzzy Inference ◽  
Population Level ◽  
Cultural Algorithms ◽  
Performance Improvements ◽  
Minimization Problems ◽  
Knowledge Based ◽  
Cultural Framework ◽  
Self Adaptation ◽  
Acceptance Function ◽  
Generalized Knowledge

Self-adaptation has been frequently employed in evolutionary computation. Angeline1 defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

CAEP: AN EVOLUTION-BASED TOOL FOR REAL-VALUED FUNCTION OPTIMIZATION USING CULTURAL ALGORITHMS

1998 ◽  
Vol 07 (03) ◽  
pp. 239-291 ◽  
Author(s):  
Chan-Jin Chung ◽  
Robert G. Reynolds
Keyword(s):  
Function Optimization ◽  
Cultural Algorithms ◽  
Performance Improvements ◽  
Minimization Problems ◽  
Adaptive Models ◽  
Cultural Systems ◽  
Self Adaptation ◽  
Acceptance Function ◽  
Population Component ◽  
Self Adaptive

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process. The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

A new approach for obtaining angular acceptance function of non-perfect parabolic concentrating collectors

Solar Energy ◽  
2017 ◽  
Vol 147 ◽  
pp. 455-462 ◽  
Author(s):  
Naum Fraidenraich ◽  
Manoel Henrique de O.P. Filho ◽  
Olga de castro Vilela
Keyword(s):  
New Approach ◽  
Angular Acceptance ◽  
Acceptance Function

A Recognition Method Using SA Optimized SVM-DS

2012 ◽  
Vol 182-183 ◽  
pp. 798-804
Author(s):  
Jun Li ◽  
Hai Bo Pu
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Estimation Method ◽  
Decision Fusion ◽  
Svm Classifier ◽  
Fusion Algorithm ◽  
Data Set ◽  
Temperature Function ◽  
Error Accumulation ◽  
Acceptance Function

Through properly setting the simulated annealing options of acceptance function, annealing function and temperature function, an adaptive hyper-parameter estimation method using simulated annealing algorithm is applied to improve the accuracy and efficiency of SVM. While, in order to eliminate the effects of error accumulation in multi-SVM, D-S theory is employed for decision fusion of SVM classifiers. When delimiting the belief and plausibility measures, recognition capability of SVM classifiers has been taken into account. And the Dempster decision rule also has been considered to the recognition result of each SVM classifier in the fusion algorithm. Finely, with the data set in the database of Statlog for the study, the experiment result indicates that this method can significantly increase the classification accuracy and demonstrate a good performance of robust.

Gap-acceptance in road traffic

1968 ◽  
Vol 5 (1) ◽  
pp. 84-92 ◽  
Author(s):  
A. G. Hawkes
Keyword(s):  
Simple Model ◽  
Service Time ◽  
Time Distribution ◽  
Road Traffic ◽  
Service Time Distribution ◽  
Major Road ◽  
Gap Acceptance ◽  
Road Vehicles ◽  
Minor Road ◽  
Acceptance Function

We find the distribution of delay to minor road vehicles waiting to merge or cross a single stream of major road traffic. The decision to cross is taken on the basis of a gap-acceptance function. The model turns out to be a simple queueing problem in which a customer finding an empty queue has a different service time distribution from queueing customers. The key to this representation is given in Section 3. Some numerical results in Section 6 indicate that in most circumstances a simple model will give adequate results.

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