in this research, pomegranate arils are dehydrated by osmotic dehydration in 40, 50, and 60 % sucrose ‎solutions and at 45, 55 and 65 degrees c‎‏ ‏‎ and weight reduction, solids grain and water loss of the products ‎were measured at 60, 120 and 180 minutes of process. osmotic dehydration processes was modeled by ‎combination of neural network and fuzzy logic techniques (neuro-fuzzy) and respons...

In this paper, we compare four methods to perform eye-to-hand visual servoing using primitive tactile information. They are relative visual servoing, direct fuzzy servoing, fuzzy correction, and interpolation. These methods just need a monocular eye-to-hand camera and primitive tactile sensors. The fuzzy methods are tuned [1] by an Adaptive Neuro-Fuzzy Inference System (ANFIS). The set-up is pa...

Fuzzy models present a singular Janus-faced: On the one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like "low-order interpolation" and "universal function approximation". Neuro-fuzzy basically provides fuzzy models with the cap...

This paper presents a new way to design a Fuzzy Terminal Iterative Learning Control (TILC) to control the heater temperature setpoints of a thermoforming machine. This fuzzy TILC is based on the inverse of a fuzzy model of this machine, and is built from experimental (or simulation) data with kriging interpolation. The Fuzzy Inference System usually used for a fuzzy model is the zero order Taka...

The original idea of reasoning and control within fuzzy rule bases was proposed by Zadeh [1], and was called the Compositional Rule of Inference (CRI) and had the disadvantage of running directly in the k-dimensional input space (where k is the number of variables) while being able however to describe multi-dimensional membership function distributions of arbitrary shape. Its modified version, ...

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions i...

Qualitative modeling of technical processes may be accomplished by dynamic fuzzy systems. A new inference method with interpolating rules is proposed as an essential basis for the analysis of this class of systems. Using this approach, the system output is dependent on both an interpolating rule derived from the fuzzy input and the fuzzy input itself. A simple example shows the typical behavior...