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Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models View Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000695</doi_batch_id><timestamp>1714004496</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 3 </title></titles><publication_date><month>12</month><year>2024</year></publication_date><journal_volume><volume>20</volume></journal_volume><issue>3</issue></journal_issue><journal_article><titles><title>Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models </title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>In this study, a new model for biomass higher heating value (HHV) prediction based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was proposed. Proximate analysis (volatile matter, fixed carbon and ash content) data for a wide range of various biomass types from the literature were used as input in model studies. Optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in order to achieve optimum prediction capability. The best-fitting model was selected using statistical analysis tools. According to the analysis, PSO-ANFIS model showed a superior prediction capability over ANFIS and GA optimized ANFIS model. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE) and coefficient of determination (R2) for PSO-ANFIS were determined as 0.3138, 0.2545, -0.00129 and 0.9791 in the training phase and 0.3287, 0.2748, 0.00120 and 0.9759 in the testing phase, respectively. As a result, it can be concluded that the proposed PSO-ANFIS model is an efficient technique and has potential to calculate biomass HHV prediction with high accuracy.</p> </p></abstract><publication_date><month>10</month><day>04</day><year>2018</year></publication_date><pages><first_page>589</first_page><last_page>597</last_page></pages><publisher_item><identifier id_type="pii">gnest_02772</identifier></publisher_item><doi_data><doi>10.30955/gnj.002772</doi><timestamp>1714004496</timestamp><resource>https://journal.gnest.org/publication/gnest_02772</resource></doi_data></journal_article></journal></body></doi_batch> </div>
