Ultrasmall two-mode dividers based on mosaic structure designed by direct-binary-search algorithm aided by artificial neural network

2020 ◽  
Author(s):  
Takeshi Fujisawa ◽  
Kunimasa Saitoh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Binary Search ◽  
Mosaic Structure ◽  
Direct Binary Search ◽  
Binary Search Algorithm ◽  
Artificial Neural

Enhanced direct binary search algorithm for binary computer-generated Fresnel holograms

2019 ◽  
Vol 58 (14) ◽  
pp. 3735 ◽  
Author(s):  
Jung-Ping Liu ◽  
Chu-Qiao Yu ◽  
Peter W. M. Tsang
Keyword(s):  
Search Algorithm ◽  
Binary Search ◽  
Direct Binary Search ◽  
Binary Search Algorithm

scholarly journals An Artificial Neural Network Genetic Algorithm with Shuffled Frog Leap Algorithm for Software Defect Prediction

2019 ◽  
Vol 8 (12S) ◽  
pp. 824-829
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Defect Prediction ◽  
Dimensional Synthesis ◽  
Software Defect ◽  
Ann Models ◽  
Artificial Neural

Defect prediction performances are significant to attain quality of the software and to understand previous errors. In this work, for assessing the classification accuracy, precision, and recall and F measure for various classifiers are used. The artificial neural network optimizations make the assumption that more than two algorithms for one optimization have been implemented. The optimization makes use of a heuristic for choosing the best of the algorithms for being applied in a particular situation. An approach of hybrid optimization for designing of the linkages method and is used for the dimensional synthesis of the mechanism. The ANN models are assisted in their convergence towards a global minimum by the multi-directional search algorithm that is incorporated in the GA. The results have shown an accuracy of classification of the NN-hybrid shuffled from algorithm to perform better by about 5.94% than that of the fuzzy classifiers and by about 3.59% of the NN-Lm training and by about 1.42% of the NN-shuffled frog algorithm..

scholarly journals Ultra-compact and efficient 1 × 2 mode converters based on rotatable direct-binary-search algorithm

2020 ◽  
Vol 28 (11) ◽  
pp. 17010 ◽  
Author(s):  
Hansi Ma ◽  
Jie Huang ◽  
Kaiwang Zhang ◽  
Junbo Yang
Keyword(s):  
Search Algorithm ◽  
Binary Search ◽  
Direct Binary Search ◽  
Mode Converters ◽  
Binary Search Algorithm

Evaluation of the Use of Artificial Neural Networks in Solar Energy

2021 ◽  
pp. 182-210
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Set Theory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Pv System ◽  
Solar Insolation ◽  
Pv Panel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) is initially used to forecast the solar insolation level and followed by the particle swarm optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel, and output voltage requirements. Genetic algorithm is a general-purpose optimization algorithm that is distinguished from conventional optimization techniques by the use of concepts of population genetics to guide the optimization search. Tabu search algorithm is a conceptually simple and an elegant iterative technique for finding good solutions to optimization problems. Simulated annealing algorithms appeared as a promising heuristic algorithm for handling the combinatorial optimization problems. Fuzzy logic algorithms set theory can be considered as a generation of the classical set theory. The artificial neural network (ANN)-based solar insolation forecast has shown satisfactory results with minimal error, and the generated PV power can be optimised significantly with the aids of the PSO algorithm.

RF MEMS for Reconfigurable Antenna Using Gravitational Search Optimization and Artificial Neural Network

2018 ◽  
Vol 27 (08) ◽  
pp. 1850132
Author(s):  
T. A. Balarajuswamy ◽  
R. Nakkeeran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rf Mems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Beam Width ◽  
Optimization Method ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The projected method explains about the problems occurred in the combination of the MEMS switches and the complete scheme plan is resolved through choosing the finest devise limits for the plan. The devise limits, namely, length of beam, width of beam, torsion arm length, switch thickness, holes and gap were measured. At this point, the finest value of the devise limit is forecast by the aid of artificial neural network (ANN). Furthermore, the method contains the optimization method of Gravitational Search Algorithm (GSA) to optimize the input signal and so dropping the Mean Square Error (MSE). The complete scheme is executed in the operational platform of MATLAB and the outcomes were examined.

Sign in / Sign up

Export Citation Format

Share Document