The hybrid neural model to strengthen the e-nose restricted in real complex conditions

Ouhmad, Slimane; Beni‐Hssane, Abderrahim; Hajami, Abdelmajid

doi:10.1016/j.procs.2018.07.150

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Whereas, the rest of the key parameters are automatically selected by our model techniques to reach the optimal network topology as detailed in the proposed model part below. Another contribution of the newly emergent idea is that it can be used in the criteria of smart city clean environment insuring green air certificate [26]. This on-line application can also help control the indoor air filtration depollution without any inaccurate information recorded in the automated systems, which has become a source of false alarms or false positive alarms for a healthy life.…”

Section: Introductionmentioning

confidence: 99%

An Electronic Nose Natural Neural Learning Model in Real Work Environment

Ouhmad

Makkaoui²,

Beni‐Hssane

et al. 2019

IEEE Access

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The natural identification of an odor, smelled once, highlights a new idea on the artificial intelligence for electronic noses to mimic as close as possible the biological olfactory system. For the first time, a natural on-line training with only one sample, to extract both eigen-weights and eigen-bias, for each odor is built herein to elaborate a natural identifier neural model in a real work environment. The proposed model efficiently reduces the maximum extent of traditional neural models complexities, namely generic work-laboratory, dimensional data learning (typically from 60 to 80%), model adaptability complication, time-consuming, heavy experiment materials and chemical products. The outcomes show that the invented model identifies correctly 100% the three hazardous contaminants of the test data (all data minus only three samples for learning) processed from gas sensors weakened in the harshest conditions. Therewith, this natural identifier model methodology is elaborated easily enough to be efficient in different environments conditions to accurately control the air quality safety in any local area. INDEX TERMS Artificial neural network (ANN), natural identifier neural learning (NINL), eigen-weights and bias, electronic nose (e-nose), sensors, highly toxic gases, real environment, air quality safety.

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Section: Introductionmentioning

confidence: 99%

An Electronic Nose Natural Neural Learning Model in Real Work Environment

Ouhmad

Makkaoui²,

Beni‐Hssane

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

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A Practical Sensors Software to Manage Fault Signals’ Impact

Ouhmad

Beni‐Hssane

Hajami

et al. 2018

Procedia Computer Science

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Description of the method of fuzzy situational control based on a composite hybrid model of a complex technical system

Avramenko

2022

Vestn. Dagest. gos. teh. univ., Teh. nauki

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Objective. The aim of the study is to generalize the accumulated experience of fuzzy situational control based on a compositional hybrid model of a complex technical system in the form of an algorithm and, on this basis, to form recommendations on the methodology for the formation and identification of situations, determining parameters and solutions for managing a complex technical system under conditions incomplete data to improve the accuracy of control decisions. Method. The use of a compositional hybrid model solves the problem of describing and modeling the system in conditions of incomplete data and the impossibility of obtaining information about the entire range of the system's operation. Fuzzy situational control makes it possible to develop control decisions in accordance with the chosen control strategy and take into account the specifics of the system thanks to the compositional model. Result. An algorithm for fuzzy situational control of complex technical systems based on compositional hybrid models is proposed. The stages, features, advantages and disadvantages of fuzzy situational control for this type of systems are considered. The procedure for determining and unambiguously identifying emerging fuzzy situations for the system is given, and a method for analyzing and developing typical control strategies is also considered. The compositional hybrid model of a complex technical system considered in the article describes the operation of the experimental compressor unit ETsK-55. Conclusion. The main advantages of the developed fuzzy situational method for managing complex technical systems include: integration of the control system with existing elements of the system; better use of available resources; adaptability and reliability of a control method based on fuzzy situational networks and a composite hybrid model of the system. Management strategies have been defined to meet the customer's requirements for product quality, as well as the safety of personnel and equipment, trouble-free production and saving resources.

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The hybrid neural model to strengthen the e-nose restricted in real complex conditions

Cited by 3 publications

References 7 publications

An Electronic Nose Natural Neural Learning Model in Real Work Environment

An Electronic Nose Natural Neural Learning Model in Real Work Environment

A Practical Sensors Software to Manage Fault Signals’ Impact

Description of the method of fuzzy situational control based on a composite hybrid model of a complex technical system

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