This paper presents the design and analysis of Neuro-Fuzzy controller based on Adaptive Neuro-Fuzzy inference system (ANFIS) architecture for Load frequency control of interconnected areas, to regulate the frequency deviation and power deviations. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to s...

Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also emp...

By using the single VGA camera installed on mobile robot, a vision-based intelligent obstacle avoidance algorithm is developed in this paper. The image data are processed by edge detection method. By using the adaptive network based fuzzy inference system (ANFIS), the horizontal edge numbers (HEN) and vertical edge numbers (VEN) are feed into ANFIS to train the fuzzy rules such as to control th...

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon th...

Abstract Electroencephagram (EEG) is the recording of electrical activity of the brain. Though it is intended to record cerebral signals,it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better diagnosis. This paper proposes a hybrid learning algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for elimin...

In pH reactors, determination and control of pH is a common problem concerning chemical-based industrial processes due to the non-linearity observed in the titration curve. We introduced a modified multiregional fuzzybased control system to overcome the complexity of precise control of pH. In order to compensate for the experimental inaccuracies in measurements of pH in-situ values; an observer...

Dissolved oxygen (DO) & COD 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 (Adaptive Neuro-Fuzzy Inference System) in water quality DO & COD prediction for the case study, Mahi river at Khanpur in Thasara Taluka of Kheda District in Gujarat State, I...

Image segmentation is very essential and critical to image processing and pattern recognition. In this paper, a technique for color image segmentation called ‘Adaptive Neuro-Fuzzy Color Image Segmentation (ANFIS)’ is proposed. Adaptive Neuro-Fuzzy system is used for automatic multilevel image segmentation. This system consists of multilayer perceptron (MLP) like network that performs color imag...

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In s...

This paper proposes an approach to tune an Adaptive Neuro Fuzzy Inference System (ANFIS) inverse controller using Iterative Learning Control (ILC). The control scheme consists of an ANFIS inverse model and learning control law. Direct ANFIS inverse controller may not guarantee satisfactory response due to different uncertainties associated with operating conditions and noisy training data. In t...