A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines

Ruiz, Frank A.; Isaza, Claudia; Agudelo, Andrs F.; Agudelo, John R.

doi:10.1016/j.engappai.2017.02.005

Cited by 31 publications

(10 citation statements)

References 28 publications

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“…The MAD parameter measures the similarity of a descriptor with the same descriptor in a class. To obtain the MADs are used probability density functions like the Gaussian function, which uses the average value of the descriptor j belonging to the class k (ρ k,j ) and its standard deviation σ k,j [30]:…”

Section: ) Marginal Adequacy Degree (Mad)mentioning

confidence: 99%

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

et al. 2021

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This paper presents the application of different approaches of intelligent controllers based on LAMDA (Learning Algorithm for Multivariate Data Analysis) to perform the trajectory tracking task of a mobile robot Pioneer 3-DX simulated in the Virtual Robot Experimentation Platform V-REP environment. The paper proposes the design of a cascade control strategy composed of two controllers, the external controller based on a feedback linearization for the robot kinematics and the internal controller applied to the robot dynamics where the LAMDA approaches are applied. In this work, it is considered an uncertain dynamic model. Thus, its identification and control are based on a reduced-order model for designing purposes. Finally, the dynamic controller is designed using different controllers based on LAMDA, such as LAMDA-PID, LAMDA-Sliding Mode Control, and Adaptive LAMDA. To perform a comparative analysis between them that includes a conventional PID, SMC, and Fuzzy-PID, different trajectories are examined to evaluate the methods. The obtained results are compared qualitatively and quantitatively to determine which proposal performs the most accurate control and presents the best results in terms of performance.INDEX TERMS LAMDA, intelligent control, mobile robot, trajectory tracking.

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Section: ) Marginal Adequacy Degree (Mad)mentioning

confidence: 99%

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

et al. 2021

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“…Considering the continuous control action as u = u c , then replacing ( 34) in (33), it is obtained:…”

Section: A Continuous Control Actionmentioning

confidence: 99%

“…This method, originally designed to work in the classification and clustering context, has focused on the identification of functional states of a system. Several works have focused on the identification of the functional state using LAMDA as tool [33]- [37]. Initially, the algorithm computes the Marginal Adequacy Degree (MAD), a parameter that measures the contribution of the descriptors of an object to each cluster/class, with fuzzy probability functions like the Binomial or Gaussian functions [34].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Sliding-Mode Control Based on Z-Numbers and LAMDA

et al. 2021

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Z-Numbers is a recent concept related to fuzzy logic where the restriction and reliability criteria are characterized as fuzzy sets. Due to the potential of Z-numbers, this paper presents the development of a fuzzy controller that combines the fundamentals of LAMDA (Learning Algorithm for Multivariate Data Analysis) with the concepts of the Total Utility of Z-numbers, to establish an inference method to improve the performance in a control system. The controller uses criteria from the sliding mode control (SMC) and the Lyapunov concepts to guarantee robustness and stability respectively. The LAMDA method is applied to compute a chattering-free control action which is applied to systems with model uncertainties and variable dynamics. The fuzzy controller has been tested by simulation in two different tasks: 1) Control of a process that consists of a mixing tank with variable dynamics, and 2) Trajectory tracking of a mobile robot. The proposed approach provides suitable results at runtime and outperforms the results of the other tested controllers in terms of performance, minimizing the deviation between the current system output and the reference. Finally, a complexity analysis is presented to evaluate the feasibility in the implementation of the proposal. The obtained results prove the suitability of using a LAMDA Z-number-based controller in the tested systems.

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“…For this reason, Model 3 was modified by increasing the number of engine cycles. When additional calculations were made for training sets, created as previously but containing averaged data from more engine cycles (15,20,25,30,35,40), the accuracy after 10-fold cross valida-tion (run once in each case) was 93.9%, 94.3%, 96.2%, 96.1%, 96.2%, and 94.3%, respectively. At the 50th run of 10-fold cross validation (with almost identical software, Linux counterpart C5.0), the averaged values amounted to 93.9%, 95.7%, 97.1%, 96.2%, 95.1%, and 93.1%, respectively.…”

Section: Application Of Decision Trees and Particle Swarm Optimizationmentioning

confidence: 99%

Archives of Acoustics

Bąkowski

Kekez

Radziszewski

et al. 2018

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Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.

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A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines

Cited by 31 publications

References 28 publications

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

A Fuzzy Sliding-Mode Control Based on Z-Numbers and LAMDA

Archives of Acoustics

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