Fuzzy Networks for Complex Systems

Gegov, Alexander

doi:10.1007/978-3-642-15600-7

Cited by 20 publications

(6 citation statements)

References 99 publications

(172 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The set of linguistic variables and fuzzy rules constitute together a Fuzzy Inference System (FIS), and the process of calculating the rules' output is known as fuzzy inference (Yen and Langari, 1999;Yager and Zadeh, 2012). A DFM can be considered as a fuzzy network (FN) (Gegov, 2010), that is, a network of interacting FISs. In a FN, nodes represent linguistic variables, while the connections among them represent fuzzy rules, whose outputs are fed as variable inputs to downstream linguistic variables.…”

Section: Dynamic Fuzzy Modelingmentioning

confidence: 99%

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

et al. 2021

View full text Add to dashboard Cite

Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.

show abstract

Section: Dynamic Fuzzy Modelingmentioning

confidence: 99%

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A DFM consists of a set of linguistic variables describing the components of the system, and a set of fuzzy rules providing a qualitative description of their interactions. A DFM can be considered as a FN [27], i.e., a network of interacting FISs. Thus, a FN can be depicted as a directed graph (as in Figure 6), where nodes represent linguistic variables, and arcs the presence of some fuzzy rules governing them.…”

Section: Repressilatormentioning

confidence: 99%

“…The first two examples illustrate how Simpful can be used to easily define the membership functions and a fuzzy rule base, and how it embeds the execution of fuzzy inference. The third example shows how Simpful can be exploited to model and simulate the dynamics of complex systems [25,26], by creating fuzzy networks (FNs) [27], i.e., networks where nodes represent linguistic variables, and the connections between them represent interactions in the form of fuzzy rule outputs fed as variable inputs to a downstream linguistic variable. These networks can be defined with arbitrary topologies, including cycles and feedback loops, to describe the interactions existing in complex systems [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Simpful: A User-Friendly Python Library for Fuzzy Logic

Spolaor¹,

Fuchs²,

Cazzaniga³

et al. 2020

IJCIS

View full text Add to dashboard Cite

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems. Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference. Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language. We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.

show abstract

“…The theory of fuzzy sets offers help for defining formal models in situations where only linguistic description is possible [67]. The idea of hierarchical fuzzy systems [19,20,48] offers special modeling potential which is a good tool for representing complex dependencies. It preserves the intuitive linguistic description of a model and also allows description of highly complicated relationships.…”

Section: Modeling Complex Ideas With Fuzzy Systemsmentioning

confidence: 99%

“…It makes this situation similar rather to multiagent architecture. However, a cascade and hierarchical fuzzy logic systems [19,20,48] may provide another insight into the behavior of the particular subsystems or mechanisms, allowing easy configuration and use of complicated sets of semirealistic features. Processing of information using a fuzzy system usually begins with a fuzzification operation and ends with defuzzification.…”

Section: Intuitive Modeling Of the Complex Functionsmentioning

confidence: 99%

OFN-Based Brain Function Modeling

Prokopowicz

Mikołajewski

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

A modeling approach may significantly help to explore the problem of weak understanding of the physiological and pathological central nervous system function in the most noninvasive and comprehensive way. The aim of this chapter is to assess and discuss the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited. IntroductionStructured networks and functional connections of interacting neural populations underlying both physiological and pathological central nervous system (CNS) function are still poorly understood. Interdisciplinarity and independence of research provide a variety of scientific approaches, used tools, and wide coverage and even overlapping of the research fields, especially as regards human research, including neuroscience [1]. The modeling approach may significantly help to explore the aforementioned problem in the most noninvasive way. The aim of this chapter is to assess and discuss the extent to which possible opportunities concerning computational brain models based on fuzzy logic techniques may be exploited.

show abstract

Fuzzy Networks for Complex Systems

Cited by 20 publications

References 99 publications

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

Simpful: A User-Friendly Python Library for Fuzzy Logic

OFN-Based Brain Function Modeling

Contact Info

Product

Resources

About