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...

in common classical methods of cavity depth estimation through microgravity data, usually when a pre-geometrical model is considered for the cavity shape, the simple geometrical models of sphere, vertical cylinder and horizontal cylinder are commonly used. it is obviously an important fact that in real conditions the shapes of the cavities are not exactly sphere, horizontal cylinder or vertical...

This paper presents prediction of liquefaction potential of soils by neuro-fuzzy models evaluated using Idriss and Boulanger method. In order to address the collective knowledge built up in conventional liquefaction method, an alternative Takagi-Sugeno-Kang reliant neuro-fuzzy model has been developed. Neuro-fuzzy is one of the artificial intelligence approaches that can be classified by machin...

The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-f...

The major prevailing challenges for Software Projects are Software Estimations like cost estimation, effort estimation, quality estimation and risk analysis. Though there are several algorithmic cost estimation models in practice, each model has its own pros and cons for estimation. There is still a need to find a model that gives accurate estimates. This paper is an attempt to experiment diffe...

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but ...

introduction: rainfall is affected by changes in the global sea level change, especially changes in sea surface temperature sst sea surface temperature and sea level pressure slp sea level pressure. climate anomalies being related to each other at large distance is called teleconnection. as physical relationships between rainfall and teleconnection patterns are not defined clearly, we used inte...

Neuro-fuzzy computing, which provides efficient information processing capability by devising methodologies and algorithms for modeling uncertainty and imprecise information, forms at this juncture, a key component of soft computing. An integrated neuro-fuzzy system is simply a fuzzy inference system trained by a neural networklearning algorithm. The learning mechanism fine-tunes the underlying...

This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical NeuroFuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity t...

This paper presents the research and development of a hybrid neuro-fuzzy model for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent neuro-fuzzy multiagent systems that use MultiAgent Reinforcement Learning...