BSFS: Design and Development of Exponential Brain Storm Fuzzy System for Data Classification

2017 ◽  
Vol 25 (02) ◽  
pp. 267-284
Author(s):  
R. Chandrasekar ◽  
Neelu Khare
Keyword(s):  
Fuzzy System ◽  
Inductive Learning ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Search Space ◽  
Exponential Model ◽  
Medical Databases ◽  
Improved Accuracy ◽  
Rule Optimization ◽  
The Brain

The inductive learning of fuzzy rule classifier suffers in the rule generation and rule optimization when the search space or variables becomes high. This creates the new idea of making the fuzzy system with precise rules leading to less scalability and improved accuracy. Accordingly, different approaches have been presented in the literature for optimal finding of fuzzy rules using optimization algorithms. Here, we make use of the brain storm optimization algorithm for rule optimization. In this paper, a new fuzzy system called, exponential brain storm fuzzy system is developed by modifying the traditional fuzzy system in rule definition process. In rule derivation, we have presented an algorithm called, EBSO by modifying the BSO algorithm with exponential model. Also, the membership function is designed using simple uniform distribution-based approach. Finally, data classification is performed with a new BSFS system using three medical databases such as, PID, Cleveland and DRD. The experimentation proved that the proposed BSFS clearly outperformed in all the three datasets by reaching the maximum accuracy.

scholarly journals Brain Storm Optimization Algorithm for Data Classification

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Shaolin Zhu ◽  
Tianqi Li ◽  
Baohuan Li
Keyword(s):  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Inductive Learning ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Search Space ◽  
Exponential Model ◽  
Brain Storm Optimization ◽  
The Brain

Abstract With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification

2018 ◽  
Vol 27 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Chandrasekar Ravi ◽  
Neelu Khare
Keyword(s):  
Real Time ◽  
Membership Function ◽  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Membership Functions ◽  
Rule Based ◽  
Genetic Fuzzy System ◽  
Brain Storm Optimization

AbstractRecently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

Nicotine as therapeutic agent in treatment of mood disorders

2016 ◽  
Vol 33 (S1) ◽  
pp. S552-S552
Author(s):  
C. Tsopelas ◽  
N. Petros ◽  
D. Maria ◽  
P. Dimitris ◽  
G.G. Angelica ◽  
...  
Keyword(s):  
Mood Disorders ◽  
Acetylcholine Receptors ◽  
Web Search ◽  
Therapeutic Agent ◽  
Negative Attitude ◽  
Medical Databases ◽  
Positive Effects ◽  
Complex Interactions ◽  
Competing Interest ◽  
The Brain

IntroductionThe plant that has as active ingredient nicotine was chewed or smoked for many years from American natives, for its therapeutic properties. Nowadays after the extensive negative attitude towards smoking, the main provider of nicotine, researchers are now pointing out the therapeutic possibilities of nicotine in mood disorders, as a substance that is acting in the acetylcholine receptors in the brain.AimsIn this review we are trying to explore the possibilities of nicotine use as a therapeutic agent.MethodsWe did a detailed research of the main medical databases, and web search engines for relevant studies. We scrutinize them independently, before reaching consensus about appropriateness for inclusion in the study.ResultsDiadermal administration of nicotine has a positive effect in depressive disorder in 3–8 days, an effect that in one study was reversed after cessation of nicotine. Patients with depression and/or healthy subjects show improvement of attention and working memory after diadermal use of nicotine. Research is not conclusive in the sustainability of these positive affects as other researchers emphasize their short effect in mood.ConclusionNicotine presents as part of novel and promising therapeutic agents with complex interactions with other neurotransmitters in the brain. Before condemning nicotine along with smoking we should acknowledge the potential use of nicotine as a therapeutic compound since research shows that some of these positive effects appear not only to smokers after abstinence but also to non-smokers.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

scholarly journals Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

2007 ◽  
pp. 123-157 ◽  
Author(s):  
Martha Carreño ◽  
Omar Cardona ◽  
Alex Barbat
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Neuro Fuzzy ◽  
Computational Intelligence Technique ◽  
Artificial Neural ◽  
Fuzzy Assessment ◽  
Rule Bases ◽  
Building Occupancy

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

scholarly journals A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 609 ◽  
Author(s):  
Marina Bardamova ◽  
Anton Konev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Rule Based ◽  
Gravitational Search ◽  
Rule Based Classifier

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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