Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.

Lithology determination from well logs with fuzzy associative memory neural network

Abstract: An artificial intelligence technique of fuzzy associative memory is used to determine rock types from well-log signatures. Fuzzy associative memory (FAM) is a hybrid of neural network and fuzzy expert system. This new approach combines the learning ability of neural network and the strengths of fuzzy linguistic modeling to adaptively infer lithologies from well-log signatures based on (1) the relationships between the lithology and log signature that the neural network have learned during the training and/or (2) geologist's knowledge about the rocks. The method is applied to a sequence of the Ordovician rock units in northern Kansas. This paper also compares the performances of two different methods, using the same data set for meaningful comparison. The advantages of FAM are: (1) expert knowledge acquired by geologists is fully utilized; (2) this knowledge is augmented by the neural network learning from the data, when available; and (3) FAM is "transparent" in that the knowledge is explicitly stated in the fuzzy rules.

Flexible complexity reduced PID-like fuzzy controllers

Abstract: In this paper, a flexible complexity reduced design approach for PID-like fuzzy controllers is proposed. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controllers is significantly reduced. However, the performance of the complexity reduced fuzzy PID controller may be degraded since the degree of freedom is decreased by the combination of input variables. To alleviate the drawback and improve the performance of the complexity reduced PID-like fuzzy controller, a flexible complexity reduced design approach is introduced in which the functional scaling factors are heuristically generated. Since the functional scaling factors are heuristically created, they can be easily adjusted for the flexible complexity reduced PID-like fuzzy controller without a priori knowledge of the exact mathematical model of the plant. Moreover, heuristic scaling factors are implemented as functionals. Therefore, the complexity of the flexible PID-like fuzzy controller will not be increased. Further, the stability of the fuzzy control system with a flexible complexity reduced PID-like fuzzy controller is discussed. Finally, the simulation results are also included to show the effectiveness of the PID-like fuzzy controller designed with the flexible complexity reduced approach.

Study and Review of Fuzzy Inference Systems for Decision Making and Control

Abstract: The fuzzy inference system with weighted average is computationally efficient and useful for dynamic nonlinear system control while the system that defuzzifies the fuzzy output into crisp is best suited for decision making and control. However in these systems complexity with high order polynomials and a substantial computational burden rises when used separately. Multilayer approach with weighted average and defuzzification set in layers can reduce the cost of defuzzification and lack of output expressivity that causes risk when used as a controller. The adaptive fuzzy system with this approach makes the system exploitable for areas requiring easy interpretation and human reasoning. In this review paper there is the study of fuzzy inference systems and the proposed multilayer system with defuzzification and weighted average is explained.

A new method for solving real and complex fuzzy systems of linear equations

Abstract: A new approach to solving fuzzy real and complex systems of linear equations based on the fuzzy center and width is presented here. The unknown variable and right-hand side vector are considered as fuzzy, whereas the coefficient matrix is considered as crisp. First the system is solved in terms of the fuzzy center, and then this solution is used with the width to get the final solution of the original system. The presented procedure is applied to analyze three example problems. The results obtained are also compared with the known solutions and are found to be in good agreement.

Apparatus and method for monitoring system health based on fuzzy metric data ranges and fuzzy rules

Abstract: A method and apparatus for determining the status of a computer system and software applications running on that system and displaying the status to a system administrator are provided. With the apparatus and method, metrics related to a particular application or subsystem are identified and then collected over a predetermined period of time using a data monitoring or collection facility to generate metric history data. Once collected, the metric history data is analyzed by computing a set of parameters representing statistical measures of the metric history data. A set of fuzzy rules are used to define the relationships between metrics and the ultimate application or subsystem status. This metric history analysis phase may be performed periodically such that the fuzzy sets are dynamically redefined at periodic intervals. The fuzzy rules are then evaluated using a fuzzy reasoning process and an overall status indication is generated. As system performance or status changes, the monitoring system can adapt by changing the shape of the “normal” fuzzy set based on the distribution of metric values. The rules may remain the same but the fuzzy set may change dynamically. This greatly reduces maintenance costs since the monitoring rule set can be slowly tuned over time, while the underlying “normal” fuzzy sets could be adjusted as often as needed. Thus, the method and apparatus provide a mechanism to express the knowledge about the key underlying relationships as fuzzy rules and then to automatically tailor the fuzzy sets that are referenced in the fuzzy rules using statistical data mining techniques.