Self-Learning ULR Fuzzy Controllers Using Temporal Back Propagation

Tang, Zheng; Tashima, Koichi; Jin, Hai; Ishizuka, Okihiko; Tanno, Koichi

doi:10.1541/ieejeiss1987.117.12_1794

Cited by 81 publications

(119 citation statements)

References 7 publications

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“…To compute the output of a model, four groups of rule based models are available (Finol et al 2001) viz. fuzzy relational models (Pedrycz 1984) linguistic models (Mandani and Assilan 1975) neural network based models (Lin and Lee, 1991;Jang, 1992) and Takagi-Sugeno-Kang (TSK) fuzzy models (Takagi and Sugeno 1985;Sugeno and Kang 1988). The most commonly used systems are Mamdani-type and Takagi-Sugeno type Geotech Geol Eng also known as Takagi-Sugeno-Kang type (Tahmasebi and Hezarkhani 2010).…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…9a, b. Governing rule set with two if-then rule of Takagi and Surgeon's type is illustrated below (Jang, 1992).…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…ANFIS performs a hybrid learning process for the estimation of premise and consequent parameters which is one of its advantages (Jang 1992). Singh et al (2005) highlights another important advantage of hybrid algorithm in the sense that it splits learning process into two independent states i.e., the adaptation of learning weights and adaptation of the nonlinear membership functions.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

et al. 2015

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The knowledge of geomechanical properties of rocks is essential for planning and design of structures in civil, mining, and other allied industries. Direct assessment of these properties is quite expensive and time consuming. It also requires fine precision in sample preparation and high-end testing instruments. To overcome these problems, 597 rock samples of four different Himalayan rock types have been tested for their strength attributes in a laboratory. The test results were correlated with their P-wave velocity using linear regression analysis for estimation of compressive, tensile, cohesive strengths and angle of internal friction. Subsequently, an adaptive neurofuzzy inference system (ANFIS) technique was applied to find its efficiency as a predictor against normal regression system. The results indicated that the ANFIS is comparatively better at prediction of geotechnical strength parameters when pitted against regression techniques.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…9a, b. Governing rule set with two if-then rule of Takagi and Surgeon's type is illustrated below (Jang, 1992).…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

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Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

et al. 2015

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“…The Takagi-Sugeno fuzzy model Rule-based models of the Takagi-Sugeno (TS) type [33] are suitable for the approximation of a broad class of functions. The TS model consists of a set of rules where the rule consequents are often taken to be linear functions of the inputs: R i : if x 1 is A i1 and … x n is A in then o i = p i1 x 1 + …, p in x n + p i(n+1), i= 1,…,M .…”

Section: Theorymentioning

confidence: 99%

A Neuro-Fuzzy Identification of ECG Beats

2010

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This paper presents a fuzzy rule based classifier and its application to discriminate premature ventricular contraction (PVC) beats from normals. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to discover the fuzzy rules in order to determine the correct class of a given input beat. The main goal of our approach is to create an interpretable classifier that also provides an acceptable accuracy. The performance of the classifier is tested on MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) arrhythmia database. On the test set, we achieved an overall sensitivity and specificity of 97.92% and of 94.52% respectively. Experimental results show that the proposed approach is simple and effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.

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“…In ANFIS, membership function parameters are extracted from a data set that describes the system behavior. ANFIS learns features in the data set and adjusts the system parameters according to a given error criterion (Jang 1992(Jang , 1993.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

2012

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Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

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Self-Learning ULR Fuzzy Controllers Using Temporal Back Propagation

Cited by 81 publications

References 7 publications

Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

Prediction of Strength Parameters of Himalayan Rocks: A Statistical and ANFIS Approach

A Neuro-Fuzzy Identification of ECG Beats

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

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