Preface

Prefac

Fuzzy Anchoring

An intelligent physical agent must incorporate motor and perceptual processes to interface with the physical world, and abstract cognitive processes to reason about the world and the options available. One crucial aspect of incorporating cognitive processes into a physically embedded reasoning system is the integration between the symbols used by the reasoning processes to denote physical objects, and the perceptual data corresponding to these objects. We treat this integration aspect by proposing a fuzzy computational theory of anchoring. Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Modeling this process using fuzzy set-theoretic notions enables dealing with perceptual data that can be affected by uncertainty/imprecision and imprecise/vague linguistic descriptions of objects

Parameter Optimization Via Genetic Algorithm Of Fuzzy Controller For Autonomous Air Vehicle

In this paper, an optimal controller for the longitudinal channel of an autonomous helicopter model is designed by blending together two artificial intelligence techniques, genetic algorithms and fuzzy control. An evaluation index that captures the complex, constrained, multiple objective character of the problem was built based on several design requirements expressed in terms of the time response of the controlled system. The parameters of the fuzzy controller are optimized to maximize the evaluation index using a genetic algorithm. The parameters subject to optimization are: the shape and width of the membership functions, number of linguistic values, defuzzification method and scaling factors. The genetic algorithm is based on binary genetic representation, a roulette wheel selection technique with elitist selection strategy and classic genetic operators: mutation and crossover. The performance of the resulting optimal controller is compared with performance obtained with standard design. Observations are made regarding influences of fuzzy controller parameters on the general performance of the controlled system