Evolution learning method to derive missing elements and optimal classification under the connection hesitant fuzzy environment

Abstract

Generally, in the qualitative decision-making process, the attributes are independent of each other, thus the given attributes' evaluation data could ignore their connection relationships and make a decision based on incomplete information. To do this, this paper first introduces the hesitant fuzzy set (HFS), an emerging evaluation tool, to develop the connection HFS (CHFS). Furthermore, considering the missing-element situation, which is a normal phenomenon in the experts' subjective evaluation process, we extend the CHFS to the missing CHFS (M−CHFS). Thus, the M−CHFS can quantitatively express the connection relationship and missing-element characteristic simultaneously. Then, to derive the missing elements in the M−CHFS evaluation information, this paper proposes the hesitant fuzzy generative adversarial network to convert the M−CHFS to the CHFS according to a deep learning process. After that, we propose a hesitant fuzzy long short-term memory network to complete the evolution learning on the obtained CHFS and then get the classification results, occurrence probabilities, and optimal classification. Then, this paper develops an evolution learning algorithm introducing the decision makers' subjective guidance and applies it to an example of intelligent manufacturing analysis. The calculation process and derived results present the feasibility and effectiveness of the given methods in this paper.