A Multiobjective Design of a Patient and Anaesthetist-Friendly Neuromuscular Blockade Controller

Fazendeiro, Paulo; Oliveira, José Valente de; Pedrycz, Witold

doi:10.1109/tbme.2007.895109

Cited by 36 publications

(14 citation statements)

References 24 publications

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“…In [91], a fuzzy logic controller for drug infusion for anesthesia was proposed. Traditionally, this administration is performed by an expert; therefore, a fuzzy controller is a good candidate to incorporate such expertise into an automatic control scheme.…”

Section: Applications On Fuzzy Controller Design Conceptmentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

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Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach to calculate a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective searching for a set of potentially preferable solutions; the designer may then analyse the trade-off among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are commented on. Through this paper it is noticed that EMO research has been developing towards different optimization statements, but such statements are not commonly used on controller tuning. Therefore gaps between EMO research and EMO applications on controller tuning are detected and suggested as potential trends for research.

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Section: Applications On Fuzzy Controller Design Conceptmentioning

confidence: 99%

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Reynoso-Meza

Blasco

Sanchis

et al. 2014

Control Engineering Practice

118

View full text Add to dashboard Cite

show abstract

“…Different fuzzy sets of a fuzzy partition should satisfy distinguishability, coverage and proper ordering [1][2][3]. In particular, for distinguishability and coverage some measures to minimize [12] will be considered:…”

Section: Interpretability Issuesmentioning

confidence: 99%

“…ε is a threshold fixed for coverage, H(·) is the Heaviside step function, M is the cardinality, and p is a positive integer (in [12], p=1). Proper ordering will be imposed by considering the strong condition of relation preservation defined in [3], since the corresponding weak condition [3] seems not enough.…”

Section: Interpretability Issuesmentioning

confidence: 99%

Insights into Interpretability of Neuro-Fuzzy Systems

Pota¹,

Esposito²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Neuro-fuzzy networks revealed their proficiency in learning from data, while offering a transparent and somehow interpretable rule-based model. Recent research focused either on the interpretability of the chosen model or on the system performance. Regarding the interpretability, here an index to control the trade-off between complexity and performance, some insights into fuzzy partitions properties, an ideal fuzzy sets shape, and an evaluation of rules are proposed. All the evaluations are made taking into account the required output and performance. A discussion on results of a system built using the Wisconsin Breast Cancer Dataset is performed as a proof of concept.

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“…In the last few years, plenty of contributions have proposed the use of MOEAs to learn the RBs (Cococcioni et al 2007;Ducange et al 2009), to select optimal subsets of the RB (Ishibuchi et al 1997;Ishibuchi and Yamamoto 2004) and to learn or tune the DBs (Botta et al 2009;Fazendeiro et al 2007) of MFRBSs. We can consider these multi-objective evolutionary fuzzy systems (MOEFSs) as the ''first generation MOEFSs'': the optimization of the RB (rule learning or selection) has been performed considering a prefixed DB and, vice versa, the tuning or learning of the DB has been carried out using a prefixed RB.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, during the last few years, some interesting partition integrity indices have been proposed in the field of MOEFSs and used in the evolutionary process (Antonelli et al 2009a, b;Botta et al 2009;Fazendeiro et al 2007;Gacto et al 2010). Following this research trend, in this paper, we propose an MOEA to generate MFRBSs with different trade-offs among accuracy, complexity and partition integrity.…”

Section: Introductionmentioning

confidence: 99%

Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity

et al. 2010

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In the last few years, several papers have exploited multi-objective evolutionary algorithms (MOEAs) to generate Mamdani fuzzy rule-based systems (MFRBSs) with different trade-offs between interpretability and accuracy. In this framework, a common approach is to distinguish between interpretability of the rule base (RB), also known as complexity, and interpretability of fuzzy partitions, also known as integrity of the database (DB). Typically, complexity has been used as one of the objectives of the MOEAs, while partition integrity has been ensured by enforcing constraints on the membership function (MF) parameters. In this paper, we propose to adopt partition integrity as an objective of the evolutionary process. To this aim, we first discuss how partition integrity can be measured by using a purposely defined index based on the similarity between the partitions learned during the evolutionary process and the initial interpretable partitions defined by an expert. Then, we introduce a three-objective evolutionary algorithm which generates a set of MFRBSs with different trade-offs between complexity, accuracy and partition integrity by concurrently learning the RB and the MF parameters of the linguistic variables. Accuracy is assessed in terms of mean squared error between the actual and the predicted values, complexity is calculated as the total number of conditions in the antecedents of the rules and integrity is measured by using the purposely defined index. The proposed approach has been experimented on six real-world regression problems. The results have been compared with those obtained by applying the same MOEA, but with only accuracy and complexity as objectives, both to learn only RBs, and to concurrently learn RBs and MF parameters, with and without constraints on the parameter tuning. We show that our approach achieves the best trade-offs between interpretability and accuracy. Finally, we compare our approach with a similar MOEA recently proposed in the literature.

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A Multiobjective Design of a Patient and Anaesthetist-Friendly Neuromuscular Blockade Controller

Cited by 36 publications

References 24 publications

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Controller tuning using evolutionary multi-objective optimisation: Current trends and applications

Insights into Interpretability of Neuro-Fuzzy Systems

Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity

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