fuzzy inference

This paper presents a new watermarking method to improve the robustness and transparency of extracted and host images. The embedding process is based on decomposing of pyramidal directional filter bank and triangular matrix, while the watermark extraction process is based on Mamdani fuzzy logic. In this design, in order to obtain efficient robustness and transparency, the Harris hawks optimization algorithm is used to find the best value of embedding factor. For this purpose, in the embedding algorithm, pyramid directional filter bank decomposition is utilized and accordingly the approximation sub-bands are divided into 8*8 non-overlapping blocks. Moreover, by decomposing the triangular matrix, which embeds the watermark bits in the matrix element, the use of Mamdani implication and the product inference engine have led to an efficient watermark extraction. The simulation results show that the quality of the watermarked image is equal to 60.6dB. Furethermore, applying the proposed algorithm is strong against attacks.

In this paper, the rules governing the behavior of the system are extracted from the system specifications and then they are modeled by Petri-nets. The set of these rules forms knowledge base of the system, which provides the ability of making inferences. Each rule consists of several premises and a conclusion. When there are many rules and the premises in different rules overlap, it is possible to prevent the repetition of premises using a hierarchical structure by the inference engine and thus reduce the number of checks required to reach the conclusion. When these rules have many and fuzzy variables, they take a complex form, and it becomes difficult to understand and deduce their behavior. To better understand this complexity, it is appropriate to visualize it using fuzzy petri-nets. So far, many different methods based on fuzzy Petri-nets have been presented to model fuzzy rules. But these methods either do not support the large number of rules and variables or do not consider matters like the role of conditional propositions in the occurrence of the conclusion propositions, the probability of the conclusion propositions, the threshold value for the conditional propositions and the conclusions, the certainty factor for the rule or for the conditional propositions. In this paper, by extending our previous work, we present a model based on fuzzy Petri-nets that covers the two mentioned cases. Finally, we present the proposed model for a secure water refinement system and the attacks against it.

Today, the widespread use of fuzzy controllers in electrical systems, especially, systems includes nonlinear and uncertainty terms, which are rapidly increasing. Indeed, the design of a desirable fuzzy inference system requires the correct selection of the type, number, and scope of membership functions due to fuzzifying the input and output variables of the system. But so far, no research or structural method has been introduced for the correct selection of membership functions and exclusively has been based on trial and error of the designer of the system. Considering the importance of this issue, the current paper investigated a comprehensive review of the membership functions selection in articles related to electrical systems. Finally, based on the articles reviewed and the categories carried out, suggestions for optimal selection of membership functions are presented based on the applications of electrical systems.

In recent years، due to the high flexibility of Fuzzy inference and suitable learning ability of Neural Networks، Neuro Fuzzy model has become a suitable model facing uncertainty and complexity in decision making and control. Meanwhile، emotional learning can be used as a suitable learning algorithm for cases that system is not fully rational. In this paper، emotional learning method based on Neuro Fuzzy model of Takagi Sugeno has been implemented which has a higher level of efficiency and flexibility comparing with other methods. The application being considered in this work was the control of heating، cooling and greenhouse system which is used in industry and agriculture and has an important effect on quality improvement of products. Such systems cannot be modeled properly using classic optimization and control methods because of complexity in dynamic structures and high level of change ratio with time and also uncertainty in their nature.