Fuzzy algorithms

Fuzzy Primal Simplex Algorithms for Solving Fuzzy Linear Programming Problems

Fuzzy Genetic Algorithms with Fuzzy Solutions

Many decisions need to be made based on imprecise or incomplete initial information. In such cases, decision makers are generally more interested in some sets of the most promising solutions rather than the best single solution. Therefore, in contrast to conventional optimisation approaches that aim to find exact optimal points, we aim to find optimal ranges with variable satisfaction degrees. ...

CISC 871: Fuzzy Logic and Fuzzy Algorithms

The applications of fuzzy logic and fuzzy algorithms to the classification of celestial objects will be explored through various prominent papers in the field. Specifically, these papers will address issues concerning star and galaxy separation, classification of stars into their spectral types, classification of galaxies into the main galaxy types, and comparing the results to their neural net...

Neuro-fuzzy Systems D1.3 Neuro-fuzzy algorithms

See the abstract for Chapter D1. Relatively early in neural network research there emerged an interest in analyzing and designing layered, feedforward networks augmented by some formalism stemming from the theory of fuzzy sets. One of B2.3 the first approaches was the fuzzification of the binary McCulloch–Pitts neuron (Lee and Lee 1975). B1.2 Then, several researchers looked at a typical feedfo...

Fuzzy Modeling Employing Fuzzy Polyploidy Genetic Algorithms

Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conven...