This paper presents a novel adaptive neuro-fuzzy based speed controller for vector controlled induction motor drive. The proposed neuro-fuzzy controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The conventional PI controller is replaced by Adaptive Neuro-Fuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid lear...

Development of Load sensor is done in this paper, the input output of the load sensor is taken from the optical fiber sensor and the inputs are load and displacement and output is voltage. Load sensor is implemented by using two models i.e. mamdani fuzzy model and neuro fuzzy model and both the models are simulated using MATLAB, Fuzzy logic Toolbox and the results of the two models are compared...

Software estimation accuracy is among the greatest challenges for software developers. This study aimed at building and evaluating a neuro-fuzzy model to estimate software projects development time. The forty-one modules developed from ten programs were used as dataset. Our proposed approach is compared with fuzzy logic and neural network model and Results show that the value of MMRE (Mean of M...

Preliminary note The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam ...

This paper presents the application of an adaptive neuro-fuzzy logic on the development of controller for 6 Degree-of-freedom parallel manipulator known as the Stewart platform. This manipulating structure has been well-known for its use as a flight simulator. In this research, the kinematics of the Stewart platform is analyzed first and then neuro-fuzzy logic based controller for controlling l...

We designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three ‘swing phase objectives’, characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been perf...

The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge, defined as human capability of making data and information useful for decision making processes, could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on s...

Classical control theory is based on the mathematical models that describe the physical plant under consideration. The essence of fuzzy control is to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy frame work has not been formalized and arbitrary cho...

A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

Abstract: In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence betw...