In this paper an accurate real-time sequence-based system for representation, recognition and analysis of lowintensity facial expressions and FAUs is presented. The feature extraction is done using facial feature point tracking and biased discriminant analysis as an efficient dimension reduction method. A novel classification scheme based on neuro-fuzyy modeling of the FAU intensity is presente...

An adaptive-network-based fuzzy inference system (ANFIS) offers a popular and powerful mechanism. As with many other advanced data-driven techniques, developing an effective ANFIS typically requires sufficient training data. However, in real-world applications, it is not always straightforward to obtain large amount of representative data that cover the entire problem space accomplish required ...

BACKGROUND An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic condition...

this study investigates the oil extraction from pistacia khinjuk by the application of enzyme.artificial neural network (ann) and adaptive neuro fuzzy inference system (anfis) were applied formodeling and prediction of oil extraction yield. 16 data points were collected and the ann was trained with onehidden layer using various numbers of neurons. a two-layered ann provides the best results, us...

In a modeling process of a real world problem, there usually are a huge number of potential inputs involved. A large number of inputs may increase the complexity in computation and cause other problems related to running time, memory spaces, etc. In the case of modeling process with large input, the number of inputs should be reduced and the priority inputs should be determined by an optimal se...

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently whe...

BOD is a parameter frequently used to evaluate the water quality on different rivers. The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adapti ve Neuro-Fuzzy Inference System) in water quality BOD prediction for the case study, Mahi river at Khanpur in Thasara Taluka of Kheda District in Gujarat State, India. The proposed technique...

Abstract This study presents the development, analysis and assessment of residential lighting load profile using computational intelligence based modelling Adaptive Neuro Fuzzy Inference System (ANFIS) and Neural network (NN) models for prediction (forecasting) and evaluation of lighting load and initiatives. Factors considered in the development of the models include natural lighting, occupanc...

Mine Blast Algorithm (MBA) is newly developed metaheuristic technique. It has outperformed Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and their variants when solving various engineering optimization problems. MBA has been improved by IMBA, which is modified in this paper to accelerate its convergence speed furthermore. The proposed variant, so called Accelerated MBA (AMBA), repla...

A supervisory Adaptive Network‐based Fuzzy Inference System (SANFIS) is proposed for the empirical control of a mobile robot. This controller includes an ANFIS controller and a supervisory controller. The ANFIS controller is off‐line tuned by an adaptive fuzzy inference system, the supervisory controller is designed to compensate for the approximation error bet...