In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modelling, is presented. Also, an adaptive neuro-fuzzy compensator compensates the friction force between the endeffector and the surface of the object. Due to the adaptive neuro-fuzzy modelling, the proposed controller is independent of the robot dynamics. Also, the stability...

This paper summaries basic principles and concepts of intelligent controls, implemented in humanoid robotics as well as recent algorithms being devised for advanced control of humanoid robots. Secondly, this paper presents a new approach neuro-fuzzy system. We have included some simulating results from our computational intelligence technique that will be applied to our humanoid robot. Subseque...

This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the basе of system’s state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simu...

Weight control system on the feeder conveyor determines the factor of the quality of products within an industry. The dynamics of the flow rate of material through the feeder conveyor weigh requires a good level of performance controllers. The base of current controllers such as FLC (Fuzzy Logic Controller) requires a certain amount of knowledge and expertise in its design that will make it dif...

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential e...

Artificial ventilation is a crucial supporting treatment for Intensive Care Unit. However, as the ventilator control becomes increasingly more complex, it is non-trivial for less experienced clinicians to control the settings. In this paper, the novel Hebbian based Rule Reduction (HeRR) neuro-fuzzy system is applied to model this control problem for intra-patient and inter-patient ventilator co...

the functions employed in an estimation of costly measured soil properties from either widely available or more easily obtained basic soil properties are referred to as pedotransfer functions. to develop pedotransfer functions, one can use multivariate regression, neural networks and neuro-fuzzy models. to make a comparison among the mentioned models, 153 soil samples were collected from soils ...

Performance of a fuzzy system identifier is investigated against a fossil fuel boiler data. A multi-layer neuro-ftrzzy system presents identification of a drum type boiler. This identification techruque provides a rule-based approach to express the boiler dynamics in fuzzy rules that are generated from the experimental boiler data. The interconnections of neuro-fuzzy layers furnish these fuzzy ...

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 ...