Fetal Electrocardiogram Extraction (FECG) identifies the congenital heart problems at the earlier stage. The major problem in the non invasive procedure is the extraction of FECG from Maternal ECG (MECG) and many interferences. The proposed methods (i) Combination of Adaptive Neuro Fuzzy Inference (ANFIS) and Fractional spline wavelet (ii) Combination of Fractional spline wavelet and ANFIS (iii...

There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher’s as well as policy maker’s point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding...

In a great diversity of knowledge areas, the variables that are involved in the behavior of a complex system, perform normally, a non-linear system. The search of a function that express those behavior, requires techniques as mathematics optimization techniques or others. The new paradigms introduced in the soft computing, as fuzzy logic, neuronal networks, genetics algorithms and the fusion of...

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in ord...

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptivenetwork-based fuzzy inference ...

Purpose Respiratory motion prediction is a chaotic time series prediction problem. In this study, respiratory motion predictability from 12 traces from breast cancer patients is examined by using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Interval Type-2 Non Singleton Fuzzy System (IT2NSFLS). Methods Free breathing data curves were obtained from Real Time Position Management system (RPM ...

The infiltration process during irrigation is an essential variable for better water management and hence there a need to develop accurate model estimate the amount irrigation. However, fact that highly non-linear procedure required special modeling approach accurately mimic procedure. Therefore, ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated furrow su...

this study investigates the prediction model of compressive strength of self–compacting concrete (scc) by utilizing soft computing techniques. the techniques consist of adaptive neuro–based fuzzy inference system (anfis), artificial neural network (ann) and the hybrid of particle swarm optimization with passive congregation (psopc) and anfis called psopc–anfis. their performances are comparativ...

Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired tempe...

Interpretability represents the most important driving force behind the implementation of fuzzy-based classifiers for medical application problems. The expert should be able to understand the classifier and to evaluate its results. The main purposes in this work is the application of a new method based on FCM and ANFIS to diagnose the diabetes diseases by using a reduced number of fuzzy rules w...