Neuro-fuzzy modeling has been intensively studied since the early nineties. Recently a method has been disclosed, that uses a classical feedforward neural network with just one hidden layer. Nodes of the hidden layer use the logistic function as activation function meanwhile the output node has a linear activation function. This paper introduces a generalization of the logistic function and eva...

This paper presents a method of imputing missing data that combines principalcomponent analysis and neuro-fuzzy (PCA-NF) modeling in conjunction with geneticalgorithms (GA). The ability of the model to impute missing data is tested using the SouthAfrican HIV sero-prevalence dataset. The results indicate an average increase in accuracyfrom 60 % when using the neuro-fuzzy model in...

The authors propose an automated neuro-fuzzy system approach (with neural network subsystem) to power quality assessment incorporating equipment susceptibility patterns. The system is expected to handle dependencies between superposition of different disturbances and specific devices’ susceptibility to disturbances. Two neural network architectures were applied: a well known radial-basis neural...

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...

An intelligent approach for smart material actuator modeling of the actuation lines in a morphing wing system is presented, based on adaptive neuro-fuzzy inference systems. Four independent neuro-fuzzy controllers are created from the experimental data using a hybrid method -a combination of back-propagation and Least-Mean-Square (LMS) methods -to train the fuzzy inference systems. The controll...

The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy–logic inference control and adaptive fuzzy–logic control, including neuro–fuzzy–fractal control. As...

Fuzzy logic and fuzzy systems have recently been receiving a lot of attention, both from the media and scientific community, yet the basic techniques were originally developed in the mid-sixties. Fuzzy logic provides a formalism for implementing expert or heuristic rules on computers, and while this is the main goal in the field of expert or knowledge-based systems, fuzzy systems have had consi...

The paper describes a Neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neurofuzzy network efficiently. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared ...

In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modeling, is presented. Since the force control involves applying certain amount of force on the surface of an object, it is important to consider the friction force between the end-effector and the surface into account. In order to compensate this friction force, a robust and...

A neuro-fuzzy modeling for forecasting the future dynamical behavior in vibration testing during satellite qualification is proposed in this paper. Vibration testing is employed for emulating vibrations present during the lifetime launching. There are different levels of excitation during vibration testing in order to verify and assure that the satellite and their sub-systems will support the e...