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# 计算机代写|机器学习代写Machine Learning代考|ENGG3300 Fuzzify Crisp Sensor Values

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## 计算机代写|机器学习代写Machine Learning代考|Fuzzify Crisp Sensor Values

Once system goals have been set, the next step is to determine the fuzzy input membership functions for each of the sensor inputs. The crisp sensor signals are provided by a series of input transducers. A input membership function is developed for each sensor as shown in Fig. 5.1. The input membership functions consist of a series of linguistic (word) variables. The span of the linguistic variables is defined by a trapezoid (or trap) function. Various forms of trap functions are illustrated in Fig. $5.2$ (Alves).

The specific trap functions are defined using the sensor profile. The crisp numerical output from the sensor is mapped to a specific linguistic variable. If the crisp numerical output from the sensor corresponds to two different linguistic variables, the linguistic variable with the smaller value is chosen.

Example: In the robot example, we use only two IR sensors to navigate through the maze. To allow the robot to detect obstacles directly in front, a front facing IR sensor is used. Also, a right facing IR sensor is used.

To design the input membership functions for the front and right IR sensor, the IR sensor profile is divided into three different zones: obstacle close, obstacle near, and obstacle far as shown in Fig. 5.3a. The IR sensor profile is used with the output sensor value to construct the input membership functions as shown in Fig. 5.3b. An input membership function is provided for both the front and right facing sensors.

## 计算机代写|机器学习代写Machine Learning代考|Apply Rules

A set of rules of the form “IF (antecedent)-THEN (consequent)” are now developed to link input membership function linguistic variables to desired output membership function values. Specific rules are developed by considering different combinations of the input membership function linguistic variables to form the antecedent. Multiple input membership function linguistic variables may be linked using “AND” and “OR” logic connectives. For the “AND” connective, the minimum value of the input membership function linguistic variable is chosen. For the “OR” connective, the maximum value of the input membership function linguistic variable is chosen. The desired output (consequent) for a given combination of input variables is determined by an expert (you-the system designer). The output membership functions are determined by linking the output linguistic variables to desired crisp numerical values.

Example: In Fig. 5.3c, we have developed the output membership functions for the left and right motor. The crisp numerical output values range from 0 to 250 . These will serve as inputs to a pulse width modulation (PWM) function to control the left and right motor speed to render different turns.

To construct the rules, linking inputs to outputs, the combination of input linguistic variables for the right and front sensor are placed in a table. The desired output for each combination of inputs is then determined by an expert (you). For example, for the right sensor at “r_close” and the front sensor at “f_close,” the desired robot action is a left medium turn (“1_med”). This is accomplished by setting the left motor to slow (“l_slow”) and the right motor to medium (“r_med”). The resulting table is provided in Fig. 5.3d.

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