International Journal of Advances in Scientific Research and Engineering (ijasre) E-ISSN : 2454-8006 DOI: 10.31695/IJASRE.2025.8.7 Volume 11, Issue 8 August - 2025 https://ijasre.net/ Page 58 Development and Control of time-related interval for ambiguous knowledge bases (Type II) using genetic algorithms Jinan Redha Mutar Department of Computer Science College of Education, Mustansiriyah University Baghdad, Iraq ______________________________________________________________________________________ ABSTRACT The Interval Type-2 Fuzzy Logic Control (IT2FLC) utilizes a genetic algorithm (GA), known as the Genetics Interval Type-2 Fuzzy Network (GIT2FS), to optimize the fuzzy parameters, including fuzzy functions for membership and fuzzy regulation bases. After a brief discussion of the genetic fuzzy system GFS, the suggested design is described. Type reductions and defuzzification are included in the output processing of interval type-2 fuzzy logic circuits. Although researchers have recently developed numerous effective type reduction techniques, there are currently no practical plans to enhance the output of defuzzification. The kind of interval type-2 fuzzy set is reduced using the type reduction algorithm presented in this paper, which also produces the best defuzzified output from the type-reduced set. The planned type reduction is also carried out offline (in other words, the controller has been reduced to type-1 in practical applications). It greatly lowers the computational expense and makes it easier, actually, to develop controllers. Problems with truck backing control are used to show the viability of the suggested approach. The study showed that, in terms of speed, computational complexity, and resilience, the suggested technique performs better than typical IT2-FLCs. Key Words: FKB, fuzzy logic, fuzzy modeling, GIT2FS, IT2FLC, LDEC. _______________________________________________________________________________________________ 1. INTRODUCTION Systems built on fuzzy information have information in the form of fuzzy rules and can be very useful in the fields of modeling, categorization, and control. These systems can better handle the uncertainties present in real-world problems due to the type 2 fuzzy set theory [1]. The authors of this study proposed a method for genetic tuning known as deflections and growth (L-D-E-C), in which the variables are calculated to modify the parameters of the interval type II membership functions. While adjusting involves compaction, adjusting deals with lateral displacement. During the creation of this technique, interpretability and reliability features were taken into account. The effectiveness of the suggested strategy is demonstrated by the experimental findings. Fuzzy algorithms, and more specifically fuzzy rule-based or experience and understanding systems, are extensively used in fields including modeling, categorization, and control. The key component of the Fuzzy Knowledge Base System (FKBS) is the combination of expanded fuzzy rules, which represent human expert knowledge, with the Obfuscation Interface, Inference System, Knowledge Base, and Misdirection Interface [2][3]. The paper keeps talking about fuzzy knowledge-based systems FK-B-S accuracy concerns in “The issues” section. It explains the fundamentals of tuning and deep learning. In " The second type of systems for fuzzy logic " part goes over the fundamentals of Type 2 fuzzy systems. In " The suggested approach for the tuning process " section goes over the new lateral displacement and development (LDEC) tuning method. In the section titled " An innovative suggestion for the G algorithm coding scheme," the proposed control strategy and the genetic depiction of the body of knowledge are described. In “The Results" section discusses the results of the experiments.