Introduction to Neural Networks and Fuzzy Logic Solutions
This content covers the solutions for fuzzy logic homework assignments, including fuzzy control scenarios and adjustments. Learn about fuzzy logic operators, rule definitions, finding solutions in Matlab, and utilizing the Fuzzy Logic Toolbox in Matlab for implementation. Gain insights into fuzzy logic methods, implications, aggregation, and defuzzification techniques to enhance understanding of neural networks and fuzzy logic applications.
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Introduction to Neural Networks and Fuzzy Logic Lecture 4 Dr.-Ing. Erwin Sitompul President University http://zitompul.wordpress.com 2 0 2 1 President University Erwin Sitompul NNFL 4/1
Fuzzy Logic Fuzzy Control Solution: Homework 3 v. small 1 0.6 0.4 small perfect big v. big growing declining constant 1 0.75 0.25 0 5 10 15 20 25 10 5 0 5 10 speed change [m/s2] distance to next car [m] 2.5 m/s2 13 m big zero +small 1 +big small 2 1 0 1 2 acceleration adj. [m/s2] President University Erwin Sitompul NNFL 4/2
Fuzzy Logic Fuzzy Control Solution: Homework 3 (Cont.) 0.4 0 Rule 1: IF distance is small AND speed is declining, THEN maintain acceleration. 0.4 0 0.75 0.4 Rule 2: IF distance is small AND speed is constant, THEN acceleration adjustment negative small. 0.6 0 Rule 3: IF distance is perfect AND speed is declining, THEN acceleration adjustment positive small. 0.6 0 0.75 Rule 4: IF distance is perfect AND speed is constant, THEN maintain acceleration. 0.6 FL-Operators: AND Min OR Max President University Erwin Sitompul NNFL 4/3
Fuzzy Logic Fuzzy Control Solution: Homework 3 (Cont.) big zero+small +big 1 small 0.6 0.4 0.4 2 (2.2 3) 1.04 0.5 c = A = + = 1 2 1 0 1 2 acceleration adj. [m/s2] 1 A = c = 0.2 2 (0.8 1.2) 0 + = 0.2 1 2 2 = 2 ( a b + ) A h A2 + + c A c A A A1 = 1 1 2 2 c x A 2 1 0 1 2 1 2 acceleration change [m/s2] 0.4194 m s = 2 President University Erwin Sitompul NNFL 4/4
Fuzzy Logic Fuzzy Control Solution: Homework 3 (Cont.) Finding Solution in Matlab Input 1 MFs Input 2 MFs Output MFs Rule Viewer President University Erwin Sitompul NNFL 4/5
Fuzzy Logic Fuzzy Control Fuzzy Logic Toolbox in Matlab The toolbox can be opened by typing fuzzy in Matlab Workspace. Some variables must be defined: Number of inputs and outputs Membership functions of each input and output Fuzzy rules that will connect the membership functions Fuzzy set operators, inference core, accumulation, and defuzzification Further complete explanation can be found in Matlab Fuzzy Toolbox User s Guide. President University Erwin Sitompul NNFL 4/6
Fuzzy Logic Fuzzy Control Fuzzy Logic Toolbox in Matlab And and OR method pairs: Min-max Prod-probor (algebraic product/sum) Implication: Min (clipping) Prod (scaling) Aggregation : max (accumulation). Defuzzification : centroid (center of gravity). President University Erwin Sitompul NNFL 4/7
Fuzzy Logic Fuzzy Control Fuzzy Logic Toolbox in Matlab Now, we utilize the fuzzy toolbox to analyze the input- output behavior of the fuzzy control. Later, the resulting fuzzy control can be applied to control dynamic systems in Simulink environment. In each session, remember to save and re-open the controller that has been designed: Save files using Files >> Export >> To Disk Open files using Files >> Import >> From Disk President University Erwin Sitompul NNFL 4/8
Fuzzy Logic Fuzzy Control Homework 4A Read the manual of Fuzzy Logic Toolbox carefully. Learn how to use the toolbox and get familiar with it. Redo the HW 3A in Matlab using Fuzzy Logic Toolbox. Print and submit the followings via Google Classroom: The screenshots of the membership functions, the rules, and the rule viewer (the rule accumulation). The *.fis file of the fuzzy control. Submission must be complete. Incomplete submission will not be graded. Deadline: On Sunday (1 day before the next class). President University Erwin Sitompul NNFL 4/9
