A fuzzy system was created in MATLAB to identify an item of clothing as a dress, shirt, or pair of pants from a series of input images. The system was initialized using a high-contrast vector-image of each item of clothing as the state closest to a direct solution. Nine other user-input images (three of each item) were also used to determine the characteristic function of each item and recogniz...

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 presents a comparative study between three versions of adaptive neuro-fuzzy inference system (anfis) algorithms and a pseudo-forward equation (pfe) to characterize the north sea reservoir (f3 block) based on seismic data. according to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

determining the distribution of heavy metals in groundwater is important in developing appropriate management strategies at mine sites. in this paper, the application of artificial intelligence (ai) methods to data analysis,namely artificial neural network (ann), hybrid ann with biogeography-based optimization (ann-bbo), and multi-output adaptive neural fuzzy inference system (manfis) to estima...

AB STRACT. The role of inferencing with uncertainty is becoming more important in rule-based expert systems (ES) , since knowledge given by a human expert is often uncertain or imprecise. We have succeeded in designing a VLSI chip which can perform an entire inference process based on fuzzy logic. The design of the VLSI fuzzy inference engine emphasizes simplicity, extensibility, and efficiency...

A function-based evaluation approach is proposed for a systematic study of fuzzy proportional-integral-derivative (PID)-like controllers. This approach is applied for deriving process-independent design guidelines from addressing two issues: simplicity and nonlinearity. To examine the simplicity of fuzzy PID controllers, we conclude that direct-action controllers exhibit simpler design properti...

A fuzzy control system which is a typical system utilizing fuzzy model is mainly using the Max-Min CRI (Compositional Rule of Inference) method by Zadeh and Mamdani for fuzzy inference. But the Max-Min CRI method suffers from drawbacks including: error-prone weighting strategy, inefficient compositional rule of inference, and subjective formulation of membership functions. Because of these prob...

Recently, we have proposed a novel intuitionistic fuzzy inference system (IFIS) of Takagi-Sugeno type which is based on Atanassov’s intuitionistic fuzzy sets (IF-sets). The IFIS represent a generalization of fuzzy inference systems (FISs). In this paper, we examine the possibilities of the adaptation of this class of systems. Gradient descent method and other special optimization methods are em...

This paper presents a Dynamic Fuzzy Q-Learning (DFQL) method that is capable of tuning the Fuzzy Inference Systems (FIS) online. On-line self-organizing learning is developed so that structure and parameters identification are accomplished automatically and simultaneously. Selforganizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuou...

Potential field methods are rapidly gaining popularity in obstacle avoidance applications for mobile robots. The general principles of potential field path planning are presented first. The electrostatic potential field (EPF) path planner is then combined with a twolayered fuzzy logic inference engine and implemented for mobile robot navigation in a 2-D dynamic environment. The first layer of t...