This paper, deals with an indirect adaptive fuzzy control of discrete-time MIMO nonlinear systems with parametric variations. The synthesis of the state feedback control law is based on the T-S fuzzy models developed by a local description of the considered system. In the first step, gradient method is used to adjust model parameters locally estimated by the fuzzy model. The state feedback cont...

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as decrease in public health. Therefore, predicting the with high degree accuracy is needed reference for government agencies community making future policies. This study aims to analyze Adaptive Neuro-Fuzzy Inference System (ANFIS) method. prediction results' level measured by valu...

Dilsaver, Steven C. and Norman F. Alessi: Temperature as a Dependent Variable in the Study of Cholinergic Mechanisms. Prog. Neuro-Psychonharmacol. & Biol. Psvchiat. 1988,

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using t...

In considering nonlinearity and uncertainty in the large erecting system, a fuzzy adaptive PID controller is introduced to improve the control performance. The mathematic model is constructed at first based on the physical laws. Then, the fuzzy logic algorithm which can regulate PID parameters on-line is designed and fuzzy inference rules are established between the PID parameters and the error...