The paper used the Adaptive-Network-based Fuzzy Inference (ANFI) to do the water consumption forecast, it not only can avoid directly inspecting the interaction between the factors and quantity, but also reduce the data random noise and improve accuracy.

In order to overcome coupling effect of aero-engine\'s control loop,a general predictive decoupling controller based on coactive adaptive network-based fuzzy inference system(CANFIS) is designed for aero-engine distributed control system with long uncertain time-delay.

Adequate datas used to train fuzzy rules and membership function in the BP neural network are obtained by means of LQR algorithm,then ANFIS controller is designed,in the end,a building is taken as the example to simulink under El Centro earthquake,the results shows that ANFIS is efficient in the structural active control.

Adaptive Neuro-Fuzzy Inference System(ANFIS) is a neural network system with one input and several outputs, which is got through achieving one step sugeno fuzzy system by the format of network.

A new superheated steam temperature control system design scheme is proposed,the main controller design is based on Adaptive Network-based Fuzzy Inference System(ANFIS).

The structure of ANFIS and the theory of noise canceling are described.

In this paper, we present an Adaptive Network-based Fuzzy Inference System (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system.

Based on captured objective input-output data couple, a new ANFIS (adaptive neural network-based fuzzy inference system) framework, which can construct fuzzy membership function and fuzzy rules in Takagi-Sugeno fuzzy model, is used to model and control the complex inverted.

fuzzy inference reasoning based AKF (FIR-AKF), neural network based AKF (NN-AKF) and adaptive neural network-fuzzy inference system based AKF (ANFIS-AKF), are chosen specially and studied.