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7. Estimate John's weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.3). • John is of medium height (degree 0.4). • John is short (degree 0.2) • John is well-built (degree 0.6). • John is weak (degree 0.30). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass) B) The fuzzy Rules are: R1: If height is short then weight is light. R2: If height is medium then weight is medium. R3: If height is tall then weight is heavy. c) The fact that John's height is 6'1"

7. Estimate John's weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.3). • John is of medium height (degree 0.4). • John is short (degree 0.2) • John is well-built (degree 0.6). • John is weak (degree 0.30). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass) B) The fuzzy Rules are: R1: If height is short then weight is light. R2: If height is medium then weight is medium. R3: If height is tall then weight is heavy. c) The fact that John's height is 6'1"

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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7. Estimate John's weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.3). • John is of medium height (degree 0.4). • John is short (degree 0.2) • John is well-built (degree 0.6). • John is weak (degree 0.30). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass) B) The fuzzy Rules are: R1: If height is short then weight is light. R2: If height is medium then weight is medium. R3: If height is tall then weight is heavy. C) The fact that John's height is 6'1"
Transcribed Image Text:7. Estimate John's weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.3). • John is of medium height (degree 0.4). • John is short (degree 0.2) • John is well-built (degree 0.6). • John is weak (degree 0.30). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass) B) The fuzzy Rules are: R1: If height is short then weight is light. R2: If height is medium then weight is medium. R3: If height is tall then weight is heavy. C) The fact that John's height is 6'1"
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