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...

Correspondence to A.R. Moghassem email: [email protected] ABSTRACT This study compares capabilities of two different modelling methodologies for predicting breaking strength of rotor spun yarns. Forty eight yarn samples were produced considering variations in three drawing frame parameters namely break draft, delivery speed, and distance between back and middle rolls. Several topologies with dif...

Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during...

Accurate and efficient estimation of streamflow in a watershed’s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference...

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...

A new method for computing the resonant frequency of the circular microstrip antenna, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. A hybrid learning algorithm is used to identify the parameters of ANFIS. The results of the new method are in excellent agreement with the experimental results reported elsewhere.