This paper proposes a rule base simplification method for fuzzy systems. The method is based on aggregation of rules with different linguistic values of the output for identical permutations of linguistic values of the inputs which are known as inconsistent rules. The simplification removes the redundancy in the fuzzy rule base by replacing each group of inconsistent rules with a single equivalent rule. The simulation results show that the aggregated fuzzy system with the consistent rule base approximates quite well the original fuzzy system with the inconsistent rule base. The main advantage of the proposed method over other methods is that it does not require any refinement of the rule base using additional data sets or expert knowledge. In this context, the method is quite suitable for applications where rule base refinement is unacceptable due to time constraints or impossible due to lack of additional data or knowledge.

No abstract

This paper proposes a novel approach for modelling complex interconnected systems by means of fuzzy networks with feedback rule bases. The nodes in these networks are rule bases connected in a feedback manner whereby outputs from some rule bases are fed as inputs to the same or preceding rule bases. The approach allows any fuzzy network of this type to be presented as an equivalent fuzzy system by linguistic composition of its nodes. The composition process makes use of formal models for fuzzy networks, basic operations in such networks, their properties and advanced operations. These models, operations and properties are used for defining several types of networks with single or multiple local and global feedback. The proposed approach facilitates the understanding of complex interconnected systems by improving the transparency of their models.

Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced.