The paper deals with devising the combination of fuzzy inference systems (FIS) and neural networks called the adaptive network fuzzy inference system (ANFIS) to determine the forming limit diagram (FLD). In this paper, FLDs are determined experimentally for two grades of low carbon steel sheets using out-of-plane (dome) formability test. The effect of different parameters such as work hardening...

An adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented in this paper. The capability of the proposed method (we named ANFIS2) for function approximation and dynamical system identification is remarkable. The structure o...

a multi objective honey bee mating optimization (hbmo) designed by online learning mechanism is proposed in this paper to optimize the double fuzzy-lead-lag (fll) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. the proposed double fll stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the ...

Accurate energy production forecasting is critical when planning for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead from run-of-river hydroelectric power plants in Turkey was introduced present study. In addition LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFI...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...

The neuro-fuzzy controller incorporates fuzzy logic algorithm with an artificial neural network (ANN) structure. The conventional PI controller is replaced by Adaptive NeuroFuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm, This makes fuzzy system training with performance of the neuro-fuzzy based vector controlled of the system under controll...

This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO2 (tons of carbon dioxide...

In this work neural and neuro-fuzzy controllers are developed for the inverters of Uninterruptible Power Supplies (UPS) to improve their transient response and adaptability to various loads. Idealized load-currentfeedback controller is built to obtain example patterns for training the networks. Example patterns under various loading conditions are used in the off-line training of the selected n...

Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and pe...