A self-healing network is a ‘dream’ for every network designer today. Abstractly it is a network that capable of maintaining the availability of network services. This would not be impossible to be turned into a reality if the networks are embedded with intelligence. By having the intelligent capability the network can be self-managed in response to events that taken place within the network it...

Interest in system identification especially for nonlinear systems has significantly increased in the past few decades. Soft-computing methods which concern computation in an imprecise environment have gained significant attention amid widening studies of explicit mathematical modelling. In this research, an adaptive neuro-fuzzy inference system (ANFIS) network design is deployed and used for m...

Nonlinear dynamic signal processing is attracting several researchers owing to its complex behavior which may be deterministic at macro level and may be in order but unruly behavior with respect to time is difficult to understand and interpret. EEG signals fall under such categories. Prediction of seizure in EEG is a challenging task. For this several prediction methodologies have been in use f...

In this paper, multi user detection in Code Division Multiple Access (CDMA) was realized with an adaptive neuro-fuzzy inference system (ANFIS) and the bit error rate (BER) performance was compared with the performances of the matched filter and a neural network receiver. Increment of the number of the active users and the receiving various user signals at the receiver input stage in different p...

The paper proposed the adaptive noise suppression technique for suppression of noise in voice communication. There are different techniques earlier used for adaptive filteration like least mean square, kalman’s filter etc.In the paper we used “fuzzy logic” technique for adaptive filteration. We know about the theory of adptive filteration of noise and application of fuzzy logic. We are using th...

An automatic neuro-fuzzy rule generation scheme is proposed for backing up navigation of carlike mobile robots. The proposed method is based on the Conditional Fuzzy C-Means (CFCM) and Fuzzy Equalization (FE) methods. The CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data, and also the FE method is used to systematically c...

IMM (Interacting Multiple Model) algorithm is widely used in target tracking, and its basic principle is described in detail at first. However, the IMM algorithm fails to obtain the prior probability of model conversion quickly and accurately when tracking for target. In this paper, an improved IMM algorithm based on ANFIS (the adaptive neural fuzzy inference system) is proposed. The improved a...

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI)...

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...

Purpose. To develop an automated classifier based on adaptive neuro-fuzzy inference system (ANFIS) to differentiate between normal and glaucomatous eyes from the quantitative assessment of summary data reports of the Stratus optical coherence tomography (OCT) in Taiwan Chinese population. Methods. This observational non-interventional, cross-sectional, case–control study included one randomly s...