In this paper, a novel self-generating neuro-fuzzy system through reinforcements is proposed. Not only the weights of the network but also the architecture of the whole network are all learned through reinforcement learning. The proposed neuro-fuzzy system is applied to the inverted pendulum system to demonstrate its performance. Key-words: reinforcement learning, neural network, neuro-fuzzy sy...

This paper introduced modeling and simulation results of an isolated solar photovoltaic system with lead-acid battery. The is operated at maximum power point tracking (MPPT) using MATLAB/Simulink environment. proposed controllers used in this are proportional – integral (PI) controller, fuzzy logic controller (FLC) Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. for controlling dc-dc...

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...

Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro-fuzzy model to predict the performance of flexible pavements using the ...

In this paper an effort has been made to design and demonstrate the use of fuzzy logic based model to predict the tensile strength of tubular joints of GI pipes which are welded with the technique of radial friction welding. The model is based on two inputs signals; rotational speed (RPM) and forge load. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique of fuzzy based systems for mode...

We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using ANFIS. The method is tested on two real-world problems: the non-linear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identi-cation using the Box and Jenkins gas furnace data 1].

The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neuro-fuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and ...

Article history: Received 20 February 2011 Accepted 4 April 2011 Available online xxxx

Maintenance plays now a critical role in manufacturing for achieving important cost savings and competitive advantage while preserving product conditions. It suggests moving from conventional maintenance practices to predictive strategy. Indeed the maintenance action has to be done at the right time based on the system performance and component Remaining Useful Life (RUL) assessed by a prognost...

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical ...