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For the given Image data below, construct a neural network model / fuzzy based model to classify the patterns (road signs) given below in Table 1.  2. Develop the mathematical model and algorithm for the above condition mentioned in #1 using PYTHON and run the simulation and get the output verified. 3. Report the output with different training tolerance, different activation function and report the output of the simulation. 4. Interpret the output

For the given Image data below, construct a neural network model / fuzzy based model to classify the patterns (road signs) given below in Table 1.  2. Develop the mathematical model and algorithm for the above condition mentioned in #1 using PYTHON and run the simulation and get the output verified. 3. Report the output with different training tolerance, different activation function and report the output of the simulation. 4. Interpret the output

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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 For the given Image data below, construct a neural network model / fuzzy based model to
classify the patterns (road signs) given below in Table 1. 

2. Develop the mathematical model and algorithm for the above condition mentioned in #1 using
PYTHON and run the simulation and get the output verified.
3. Report the output with different training tolerance, different activation function and report the
output of the simulation.
4. Interpret the output of the designed algorithm with respect to change in training parameters. 

1. For the given Image data below, construct a neural network model / fuzzy based model to classify the patterns (road signs) given below in Table 1. Table 1: Input/ Output Objects/ X1 X2 X3 X4 Y1 Y2 Pattern U turn Sign U turn Sign U turn Sign U turn Sign U turn Sign One way One way One way One way One way 0.35 0.54 0.32 0.28 0.33 0.57 0.21 0.27 0.85 0.73 0.66 0.26 0.80 0.72 0.61 0.26 0.77 0.70 0.55 0.22 0.54 0.81 0.88 0.66 1 0.53 0.63 0.86 0.69 1 0.52 0.34 0.74 0.61 1 0.48 0.55 0.77 0.63 1 0.54 0.81 0.88 0.60 1 80 KM 0.11 0.11 0.25 0.51 0.88 0.92 1 80 KM 0.22 0.68 1 80 KM 0.33 0.62 0.67 0.83 1 80 KM 0.23 0.66 0.45 0.86 1 80 KM 0.22 0.68 0.51 0.83 1 1 Stop Stop Stop Stop Stop 0.51 0.53 0.63 0.45 1 0.62 0.62 0.66 0.43 1 1 0.63 0.67 0.68 0.42 1 1 0.55 0.62 0.61 0.46 1 1 0.51 0.53 0.63 0.47 1 1
Transcribed Image Text:1. For the given Image data below, construct a neural network model / fuzzy based model to classify the patterns (road signs) given below in Table 1. Table 1: Input/ Output Objects/ X1 X2 X3 X4 Y1 Y2 Pattern U turn Sign U turn Sign U turn Sign U turn Sign U turn Sign One way One way One way One way One way 0.35 0.54 0.32 0.28 0.33 0.57 0.21 0.27 0.85 0.73 0.66 0.26 0.80 0.72 0.61 0.26 0.77 0.70 0.55 0.22 0.54 0.81 0.88 0.66 1 0.53 0.63 0.86 0.69 1 0.52 0.34 0.74 0.61 1 0.48 0.55 0.77 0.63 1 0.54 0.81 0.88 0.60 1 80 KM 0.11 0.11 0.25 0.51 0.88 0.92 1 80 KM 0.22 0.68 1 80 KM 0.33 0.62 0.67 0.83 1 80 KM 0.23 0.66 0.45 0.86 1 80 KM 0.22 0.68 0.51 0.83 1 1 Stop Stop Stop Stop Stop 0.51 0.53 0.63 0.45 1 0.62 0.62 0.66 0.43 1 1 0.63 0.67 0.68 0.42 1 1 0.55 0.62 0.61 0.46 1 1 0.51 0.53 0.63 0.47 1 1
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