الفهرس 
Neural Networks Research
Multivariable ANFIS with Immune Genetic AlgorithmOnly 14 pages are availabe for public view
 |  | from | 238 |  |  |
| | | from | 238 | | |
Abstract
This thesis aims at introducing an adaptive neuro-fuzzy inference (ANFIS) controller for non-linear dynamical systems. To achieve this goal, two proposed schemes that take into consideration the interactions between the variables of the controlled process are proposed. Indeed, these types of controllers are more complicated than the simple multi-input multi-output (MIMO) ANFIS controllers. The first proposed scheme is a multivariable ANFIS (MY -ANFIS) controller, which consists of a set of control loops each one is a single-input single-output (SISO) ANFIS controller with three fuzzy sets. The second proposed scheme is a cascaded distributed multi-variable ANFIS (CD-MY -ANFIS) controller that can overcome the dimensionality problem of complex fuzzy rules model. The cascading process in this scheme reduces the complexity of the fuzzy rules model by dividing the controlling system into groups of cascaded sub-systems each one has a multi-input single-output (MISO) ANFIS, which by turn reduces 70% of the fuzzy rules. To enhance the performance of the proposed controllers, the controllers’ parameters are tuned by an artificial immune system (AIS), afterwards these controllers are tested and implemented on two different non¬linear control processes: the drum boiler turbine units and the greenhouse system.