Membership function circuit for neural/fuzzy hardware of analog-mixed operation based on the programmable conductance

A new method for handwriting identification was presented.Individual characters was separated from a word choosed from a paragraph of handwritten text image which is given as input to the system. Then each of the separated characters are converted into column vectors of 625 values that are later fed into the adaptive neural fuzzy inference system(ANFIS), which was calculate membership function(MF) and normalized firing strength.In our paper we were used triangular membership function and compare with others MF.The networks has been designed with single layered neural network corresponding to a character from a-z, the outputs of all the column vector is fed into network the which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of column vector thus providing us with recognized outputs with great efficiency.

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Quantum-Membership-Function-Based Adaptive Neural Fuzzy Inference System

Intelligent Technologies and Engineering Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-1-4614-6747-2_28 ◽

2013 ◽

pp. 227-233 ◽

Cited By ~ 2

Author(s):

Cheng-Hsiung Chiang

Keyword(s):

Membership Function ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Neural Fuzzy

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Adaptive Neural Fuzzy Inference System and Automatic Clustering for Earthquake Prediction in Indonesia

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.1.204 ◽

2019 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Mohammad Nur Shodiq ◽

Dedy Hidayat Kusuma ◽

Mirza Ghulam Rifqi ◽

Ali Ridho Barakbah ◽

Tri Harsono

Keyword(s):

Earthquake Prediction ◽

Membership Function ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

The United States ◽

Earthquake Risk ◽

Eurasian Plate ◽

Inference System ◽

Automatic Clustering ◽

Neural Fuzzy

Earthquake is a type of natural disaster. The Indonesian archipelago located in the world's three mega plates; they are Australian plate, Eurasian plate, and Pacific plate. Therefore, it is possible for applied of earthquake risk of mitigation. One of them is to provide information about earthquake occurrences. This information used for spatiotemporal analysis of earthquakes. This paper presented Spatial Analysis of Magnitude Distribution for Earthquake Prediction using adaptive neural fuzzy inference system (ANFIS) based on automatic clustering in Indonesia. This system has three main sections: (1) Data preprocessing, (2) Automatic Clustering, (3) Adaptive Neural Fuzzy Inference System. For experimental study, earthquake data obtained Indonesian Agency for Meteorological, Climatological, and Geophysics (BMKG) and the United States Geological Survey’s (USGS), the year 2010-2017 in the location of Indonesia. Automatic clustering process produces The optimal number of cluster, that is 7 clusters. Each cluster will be analyzed based on earthquake distribution. Its calculate the b value of earthquake to get the seven seismicity indicators. Then, implementation for ANFIS uses 100 training epochs, Number of membership function (MFs) is 2, MFs type input is gaussian membership function (gaussmf). The ANFIS result showed that the system can predict the non-occurrence of aftershocks with the average performance of 70%.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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