Mobile pollution source emission concentration prediction method based on fuzzy weighting ELM

Abstract

The invention discloses a mobile pollution source emission concentration prediction method based on a fuzzy weighting ELM. According to the method, on the basis of spatial and temporal distribution features of pollutants from a mobile pollution source, a self-adaptive fuzzy weighting extreme learning machine model is provided; according to the features of a fuzzy system, partial input data of a mobile pollution source emission concentration data set is utilized to complete initialization of a self-adaptive fuzzy weighting extreme learning machine, namely fuzzification processing. Due to the self-learning and self-adjustment features of the fuzzy system, input weight values and implicit strata offset values of the extreme learning machine can be optimized, and then regularization processingis carried out by the weighting extreme learning machine to obtain the mobile pollution source emission concentration prediction method.

Description

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AI Technical Summary

Benefits of technology

The predicting methods described by this inventor use a special type of algorithm called Fuzzy System (FS) that helps with smoother data processing. By doing it correctly, these algorithms make predictions more accurate even at faster speeds than traditional techniques like linear regression or neural networks. Additionally, they are able to handle complex datasets without requiring too much time for training on new dataset versions. Overall, their technical effect will be improved efficiency and speediness when making inferences from large amounts of unstructured data.

Problems solved by technology

The technical problem addressed in this patents relates to accurately detecting and controlling gaseous pollutions released into indoor environments due to their harmful effects on humans' wellbeing or other living beings. This can lead to increased levels of particulate matter, nitrous oxide, hydrocarbons, carboxylic acids, formaldehydes, methane, ethanolamines, benzo(a)-pyrene, chloropropenes, tetrafluorobenzene, peroxynitrite, sulfur dioxins, cyclohexenone, hexavalinum albuminosilicate, polysaccharides, bacteria, fungi, yeasts, feces, seafood residue, ammonium salts, calcium carbonate, iron powder, manganese silica, magnesite, zinc selenites, cathode ray tubes, radon blackening agents like uranium-235marsocalculums, radioactive materials, chromosomally altered cells, cancer cell lines, tumours, and even terrorist attacks involving these substances have been identified worldwide through investigative studies conducted over several decades ago. Current approaches involve analyzing data collected during regular surveillance campaigns and estimating cumulative exposure rates associated therewith, which may result in incorrect determinations when determining whether an outbreak needs urgent intervention measures.

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