Fuzzy Controller for Flatness Based on Neural Network Pattern-recognition
نویسندگان
چکیده
A pattern-recognition method for flatness defect based on CMAC neural network is proposed, and a flatness fuzzy controller based on the pattern-recognition results is designed in this paper. Pattern-recognition and controller are designed into a single unit, in which CMAC recognizes the membership grade relative to six basic modes of common flatness defect and realizes the seeking function of the membership grade as the forepiece of the fuzzy controller for flatness directly. Through analyzing the characteristics of the flatness defect, the fuzzy set is defined reasonably, which has greatly reduced the calculation amount of fuzzy reasoning. The result of simulation shows that the pattern -recognition method of flatness offers high recognizing precision, the designed fuzzy controller for flatness can control the flatness defect to expected goal fleetly and the performance of flatness control is fine.
منابع مشابه
Pattern Recognition in Control Chart Using Neural Network based on a New Statistical Feature
Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...
متن کاملApplication of Pattern Recognition Algorithms for Clustering Power System to Voltage Control Areas and Comparison of Their Results
Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...
متن کاملApplication of Pattern Recognition Algorithms for Clustering Power System to Voltage Control Areas and Comparison of Their Results
Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...
متن کاملEffect of Distributed Power-Flow Controller (DPFC) on Power System Stability
Distributed flexible AC- transmission system (D-FACTS) is a recently advanced FACTS device with high flexibility and smaller size. The DPFC can control power flow in transmission lines, regulate bus voltages and it can also enhance stability margin in power grids. Adaptive-neural network-based fuzzy inference system (ANFIS) combines features of artificial neural network and fuzzy controller. Th...
متن کاملMaximum Power Point Tracking of the Photovoltaic System Based on Adaptive Fuzzy-Neural Method
The aim of this paper was to present an optimized method in order to use maximum capacity of the photovoltaic panels. In this regard, we presented a method for the maximum power point tracking in the photovoltaic systems by using the neural networks and adaptive controller. In the proposed system, we estimated an error by using neural network. If this error is lower than the allowable systems e...
متن کامل