LINEAR ANTENNA SYNTHESIS WITH A HYBRID GENETIC ALGORITHM

As far as taking-away of the symmetry constraints is concerned, as a scientifically symmetry problem, the global synthesis for antenna arrays that produce the desired radiation pattern is also a highly nonlinear optimization issue in fact. Besides this, the built criteria offer the reasonable power patterns. The consequent synthesis could be implemented by looking for a nominal pattern. When the criteria are already sufficient, it can simply do the whole synthesis process. To utilize multiple antennae, a method to choose a transmit antenna for the linear dispersion codes (LDC-TAS) is implemented in this paper. The authors used the max–min-post- signal to noise ratio (SNR) criteria to select these optimal transmitting antennae while this dependent, linear receiver is applied to the varying and slow channel. The simulated results illustrate that this max–min-post-SNR criterion outperforms the Bell Labs layered space time transmitting antenna selection (BLAST-TAS) applying the same spectral efficiency than space–time block codes (STBC)-TAS in the environment with low SNR. Furthermore, once the M antennae are selected under the selection criteria, a max–min-post-SNR rule, a novel linear antenna synthesis to linear dispersion codes on the basis of an innovative HYBRID (of mixed characters or solutions) genetic algorithm has been presented and evaluated to formulate and address the optimal problem to non-uniformly spaced and linear arrays. The restricted side-lobes level, the main-lobe width, and the shaped beam pattern are contemporarily concerned via maximizing a pretty suitable cost function through the innovational advanced genetic-algorithm-based algorithm. The method proposed in this paper can provide flexibility and a simple insertion of the a priori knowledge under a small computing pressure. At last, a computing simulation is completed well and the results are shown. It should be noticed that some extensions of the presented method could also be easily utilized without an obvious increase in the algorithm complexity.

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Linear Antenna Synthesis with a Fuzzy Genetic Algorithm

EUROCON 2007 - The International Conference on "Computer as a Tool" ◽

10.1109/eurcon.2007.4400530 ◽

2007 ◽

Cited By ~ 4

Author(s):

B. Kadri ◽

F.T. Bendimered

Keyword(s):

Genetic Algorithm ◽

Linear Antenna ◽

Antenna Synthesis ◽

Fuzzy Genetic Algorithm ◽

Fuzzy Genetic

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Evaluation of Accidental Death Records Using Hybrid Genetic Algorithm

SSRN Electronic Journal ◽

10.2139/ssrn.3563084 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nikhil Sharma ◽

Ila Kaushik ◽

Rajat Rathi ◽

Santosh Kumar

Keyword(s):

Genetic Algorithm ◽

Hybrid Genetic Algorithm ◽

Accidental Death

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Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

Recent Patents on Engineering ◽

10.2174/1872212112666180115162726 ◽

2019 ◽

Vol 13 (2) ◽

pp. 159-165

Author(s):

Manik Sharma ◽

Gurvinder Singh ◽

Rajinder Singh

Keyword(s):

Genetic Algorithm ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Opinion Mining ◽

Hybrid Genetic Algorithm ◽

Online Reviews ◽

Middle Tier ◽

Complete Set ◽

Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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