Zadah in [1] introduced the notion of a fuzzy subset A of a nonempty set S as a mapping from S into [0,1], Liu in [2] introduced the concept of a fuzzy ring, Martines [3] introduced the notion of a fuzzy ideal of a fuzzy ring. A non zero proper ideal I of a ring R is called an essential ideal if I  J  (0), for any non zero ideal J of R, [4]. Inaam in [5] fuzzified this concept to essential fuzzy ideal of fuzzy ring and gave its basic properties. Nada in [6] introduced and studied notion of semiessential ideal in a ring R, where a non zero ideal I of R is called semiessential if I  P  (0) for all non zero prime ideals of R, [4]. A ring R is called uniform if every ideal of R is essential. Nada in [6] introduced and studied the notion semiuniform ring where a ring R is called semiuniform ring if every ideal of R is semiessential ideal. In this paper we fuzzify the concepts semiessential ideal of a ring, uniform ring and semiuniform ring into semiessential fuzzy ideal of fuzzy ring, uniform fuzzy ring and semiuniform fuzzy ring. Where a fuzzy ideal A of a fuzzy ring X is semiessential if I  P  (0) for any prime fuzzy ideal P of X. A fuzzy ring X is called uniform (semiuniform) if every fuzzy ideal of X is essential (semiessential) respectively. In S.1, some basic definitions and results are collected. In S.2, we study semiesential fuzzy ideals of fuzzy ring, we give some basic properties about this concept. In S.3, we study the notion of uniform fuzzy rings and semiuniform fuzzy rings. Several properties about them are given. Throughout this paper, R is commutative ring with unity, and X(0) = 1, for any fuzzy ring.
The control charts are one of the scientific technical statistics tools that will be used to control of production and always contained from three lines central line and upper, lower lines to control quality of production and represents set of numbers so finally the operating productivity under control or nor than depending on the actual observations. Some times to calculating the control charts are not accurate and not confirming, therefore the Fuzzy Control Charts are using instead of Process Control Charts so this method is more sensitive, accurate and economically for assisting decision maker to control the operation system as early time. In this project will be used set data fr
... Show More
Quality is one of the important criteria to determine the success of product. So quality control is required for all stages of production to ensure a good final product with lowest possible losses. Control charts are the most important means used to monitor the quality and its accuracy is measured by quickly detecting unusual changes in the quality to maintain the product and reduce the costs and losses that may result from the defective items. There are different types of quality control charts and new types appeases involving the concept of fuzziness named multinomial fuzzy quality control chart (FM) , dividing the product to accepted and not may not be accurate therefore adding fuzziness concept to quality charts confirm and a
... Show More
(1)
In this article, we aim to define a universal set consisting of the subscripts of the fuzzy differential equation (5) except the two elements and , subsets of that universal set are defined according to certain conditions. Then, we use the constructed universal set with its subsets for suggesting an analytical method which facilitates solving fuzzy initial value problems of any order by using the strongly generalized H-differentiability. Also, valid sets with graphs for solutions of fuzzy initial value problems of higher orders are found.
(1)
(1)
conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation.
This paper proposes a self organizing fuzzy controller as an enhancement level of the fuzzy controller. The adjustment mechanism provides explicit adaptation to tune and update the position of the output membership functions of the fuzzy controller. Simulation results show that this controller is capable of controlling a non-linear time varying system so that the performance of the system improves so as to reach the desired state in a less number of samples.
This paper presents a fuzzy logic controller for a two-tank level control system, which is a process with a dead time. The fuzzy controller is a proportional-integral (PI-like) fuzzy controller which is suitable for steady state behavior of the system. Transient behavior of the system was improved without the need for a derivative action by suitable change in the rule base of the controller. Simulation results showed the step response of the two-tank level control system when this controller was used to control this plant and the effect of the dead time on the response of the system.
A new de-blurring technique was proposed in order to reduced or remove the blur in the images. The proposed filter was designed from the Lagrange interpolation calculation with adjusted by fuzzy rules and supported by wavelet decomposing technique. The proposed Wavelet Lagrange Fuzzy filter gives good results for fully and partially blurring region in images.