User:Goh Cai Ni

Demonstration on LFE and BLS

There are many type of Automated Method in fuzzy logic, these Automated Methods can be categorized into 2  types, Fuzzy Modelling Method and Refining Methods. Fuzzy Modelling Method is used to develop fuzzy systems from training data while Refining Method is to take fuzzy systems that have been developed by the Fuzzy Modelling Method and refine them with additional training data. From the data, the rules will be derived out and combined with expert rules to provide a contextual meaning to the underlying physics of the problem. One of the most common used method in Fuzzy Modelling Method is Learn From Experience Technique (LFE). Just like it's name, the model can learn from experience data obtained by trial-and-error of a task and it can stably learn from both experiences of success and failure of a trial. the learning of the model is executed after each of trial of the task. In Refining Method, Batch Least Squares Algorithm (BLS) is the most common used in numerical computation. Essentially it is a technique for solving a set of equations where there are more equations than unknowns for example an over determined set of equations. As it has the ability on enhancing or improving existed rules, it is helpful to have knowledge about the pattern of the data set in order to form a rule-base. In the demonstrator of LFE & BLS, we get the Matlab code was developed by Jung J. Kim and M. M. Reda Taha. Firstly we need the generator.m code which is for the basic set up based on our dataset and requirement before we apply the training. The generator.m will generate a .m file called "LFS_INP.m" which stores the basic setting we has did just now.

To see the step by step of demonstrating on LFE and BLS, please click the link below : https://github.com/caini1213/Demonstration-on-LFE-and-BLS

Video explanation : https://drive.google.com/file/d/1db4JUioZapQBO254PtVo_dvH3UW3ADR8/view?usp=sharing