Using Tagaki-Sugeno method, expert fuzzy system is designed for color detection which is most crucial part of the project.
The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables.
Moreover, as a formal methodology to emulate the intelligent decision-making process of a human expert, fuzzy logic control provides an effective and flexible way to arrive at a definite conclusion based on imprecise information.
Its Fome Zero (zero hunger) programme captured the imagination in Brazil and outside, but is damned by many experts as fuzzy and outmoded.
Intelligent control techniques consist of knowledge-based expert or fuzzy logic control.
Major advances are inclusions of methods of expert knowledge (fuzzy models) and extract models from examples (such as neural networks).
We designed an expert-based fuzzy MA classifier in which rule induction was performed by particle swarm optimization.
The system is designed to simultaneously support multiple heterogeneous intelligent methodologies, such as neural networks, expert systems, fuzzy logic, neural networks and genetic algorithms.
The IDSM will integrate multi-criteria decision making via intelligent technologies like expert systems, fuzzy logic, machine learning and genetic algorithms.
Due to inherent impreciseness and vagueness of expert judgement, fuzzy TOPSIS methodology was developed.
In this study, we assumed that there is no precise knowledge about the contractile behavior of an isolated artery, which can form the basis for an expert-designed fuzzy logic system or mechanistic model.
