Type-2 GA-TSK fuzzy neural network

Cai, Aaron; Quek, Chai; Maskell, Douglas L.

doi:10.1109/cec.2007.4424661

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…A Type-2 FIS consists of 4 parts: fuzzifier, knowledge base or rules, fuzzy inference machine and output processor, as Figure 10 shows. A reduction of the type is necessary in the output processor to convert a dataset from Type-2 to Type-1 [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

Castañón–Puga

Salazar

Aguilar

et al. 2015

Sensors

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The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi–Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.

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Section: Methodsmentioning

confidence: 99%

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

Castañón–Puga

Salazar

Aguilar

et al. 2015

Sensors

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“…In type-2, a type reducer is needed in the output processor to derive a type-1 set from the type-2 set [3]. Figure 1 shows a block diagram of the classic structure of a Mamdani and Takagi-Sugeno Fuzzy Logic System.…”

Section: Mamdani and Takagi-sugeno Fuzzy Logic Systemmentioning

confidence: 99%

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Castañón–Puga¹,

Flores-Parra²,

Castro³

et al. 2015

Procedia Computer Science

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This paper introduces JT2FISClustering, a data mining extension for JT2FIS. JT2FIS is a Java class library for building intelligent applications. This extension is used to extract information from a data set and transform it into an Interval Type-2 Fuzzy Inference System in Java applications. Mamdani and Takagi-Sugeno Fuzzy Inference Systems can be generated using fuzzy c-means or subtractive data mining methods. We compare the outputs and performance of Matlab R versus Java in order to validate the proposed extension.

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“…In offline learning, the structure of the network is fixed and the rules are often initialized employing offline clustering algorithms. In the literature many algorithms employ an offline learning mode in type-2 fuzzy systems [124,125,129,[166][167][168][169][170].…”

Section: Batch/ Offline Learning Modementioning

confidence: 99%

“…In [129] In the literature, various other algorithms have been proposed [125,167], where a gradient descent based learning approach is employed to tune the parameters of type-2 systems and a genetic algorithm based approach is used to search the accurate spread of uncertainties. In [125], an interval type-2 fuzzy neural network has been proposed that employs a genetic algorithm to tune the mean and deviation in the IT2FNN.…”

Section: Batch/ Offline Learning Modementioning

confidence: 99%

“…Following this work, various works have been proposed that employ different variants of evolutionary algorithms to tune the parameters of the network. In [167], a type-2 fuzzy neural network has been proposed that uses genetic algorithm based method for tuning the parameters of the network. Here, type-2 fuzzy sets handle uncertainties and the genetic algorithm based tuning achieves better classification results.…”

Section: Batch/ Offline Learning Modementioning

confidence: 99%

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Self-regulating interval type-2 neuro-fuzzy inference system for non-stationary EEG signal processing

Das¹

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Motor-imagery based Brain Computer Interface (BCI) provides a direct communication pathway between the brain and a computer based on the neural activities generated by the brain. Such a technology enables people with physical disabilities to communicate with the external world without using their peripheral nerves and muscles. Moreover, BCI systems can also be used in field of gaming, robotics and human-computer interaction, in general. A typical motor-imagery based BCI system consists of a brain activity acquisition phase to obtain ElectroEncephaloGram (EEG) data using multiple channels, a brain signal processing phase to decode the user intentions and a translation phase for transforming the decoded information to control external devices. However, the usage of multiple channels may lead to several issues: a) Longer preparation time; b) Redundant channels with noisy information. In addition, the thoughts of users vary, which may change the data distribution over time (leads to non-stationary nature of EEG). Some of the trials are also affected by the presence of artifacts in EEG data. Further, the frequency response for a motor-imagery task varies for different individuals. These issues can render the BCI system inaccurate with deteriorated performance. The main goal of this thesis is to develop a unified BCI system that address the above mentioned issues. There are four major contributions in this thesis. The first contribution is development of a Robust Common Spatial Pattern (RoCSP) feature extraction algorithm that eliminates the trials affected by artifacts and discards the redundant channels to improve the classification performance of the BCI system. Experimental results show that RoCSP is able to reduce the number of channels and produce a good classification performance. Although, RoCSP provides robust features and improves performance, it does not handle non-stationarity in the EEG data. Recently, it has been shown that interval type-2 fuzzy systems are capable of handling non-stationarity. However, there is a challenge vii

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Type-2 GA-TSK fuzzy neural network

Cited by 12 publications

References 16 publications

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals

Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

Self-regulating interval type-2 neuro-fuzzy inference system for non-stationary EEG signal processing

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