A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and weight. The sensor system is somehow rather primitive: • Shape sensor : -1 if the fruit is round and 1 if it is more elliptical • Texture sensor : -1 if the surface is smooth, 1 if it is rough • Weight sensor : -1 if the fruit is > 500g, 1 if is < 500g The sensor output will then be input to a Neural Networks based classifying system. As an AI Engineer you are supposed to design (draw the architecture and determine the optimal weight W and bias b) a simple neural network (could be a single perceptron) that can be used to recognize the fruit so that it can be directed to the correct storage bin. As a startup case, the simple nctwork will only be used for two types of fruit i.e. banana and apple. Employ initial weight W= (0.5 -1.0 -0.5) and b = 0.5. Datasets from the sensor are as follows: banana = (-1, 1, -1); apple = (1, 1, %3D -1).
A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and weight. The sensor system is somehow rather primitive: • Shape sensor : -1 if the fruit is round and 1 if it is more elliptical • Texture sensor : -1 if the surface is smooth, 1 if it is rough • Weight sensor : -1 if the fruit is > 500g, 1 if is < 500g The sensor output will then be input to a Neural Networks based classifying system. As an AI Engineer you are supposed to design (draw the architecture and determine the optimal weight W and bias b) a simple neural network (could be a single perceptron) that can be used to recognize the fruit so that it can be directed to the correct storage bin. As a startup case, the simple nctwork will only be used for two types of fruit i.e. banana and apple. Employ initial weight W= (0.5 -1.0 -0.5) and b = 0.5. Datasets from the sensor are as follows: banana = (-1, 1, -1); apple = (1, 1, %3D -1).
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
Section: Chapter Questions
Problem 1PE
Related questions
Question
![A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of
sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is
then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and
weight. The sensor system is somehow rather primitive:
• Shape sensor : -1 if the fruit is round and 1 if it is more elliptical
• Texture sensor : -1 if the surface is smooth, 1 if it is rough
• Weight sensor : -1 if the fruit is > 500g, 1 if is < 500g
The sensor output will then be input to a Neural Networks based classifying system. As an AI
Engineer you are supposed to design (draw the architecture and determine the optimal weight W
and bias b) a simple neural network (could be a single perceptron) that can be used to recognize
the fruit so that it can be dirccted to the correct storage bin. As a startup case, the simple network
will only be used for two types of fruit i.e. banana and apple. Employ initial weight W = (0.5
-1.0 -0.5) and b= 0.5. Datasets from the sensor are as follows: banana = (-1, 1, -1); apple = (1, 1,
%3D
-1).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc5fa15ba-48ad-4034-906a-bdc49bb8b25d%2F87464a4e-a259-4d64-958a-633e212e057c%2Fyq3hmu_processed.jpeg&w=3840&q=75)
Transcribed Image Text:A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of
sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is
then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and
weight. The sensor system is somehow rather primitive:
• Shape sensor : -1 if the fruit is round and 1 if it is more elliptical
• Texture sensor : -1 if the surface is smooth, 1 if it is rough
• Weight sensor : -1 if the fruit is > 500g, 1 if is < 500g
The sensor output will then be input to a Neural Networks based classifying system. As an AI
Engineer you are supposed to design (draw the architecture and determine the optimal weight W
and bias b) a simple neural network (could be a single perceptron) that can be used to recognize
the fruit so that it can be dirccted to the correct storage bin. As a startup case, the simple network
will only be used for two types of fruit i.e. banana and apple. Employ initial weight W = (0.5
-1.0 -0.5) and b= 0.5. Datasets from the sensor are as follows: banana = (-1, 1, -1); apple = (1, 1,
%3D
-1).
Expert Solution
![](/static/compass_v2/shared-icons/check-mark.png)
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 3 steps with 2 images
![Blurred answer](/static/compass_v2/solution-images/blurred-answer.jpg)
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.Recommended textbooks for you
![Database System Concepts](https://www.bartleby.com/isbn_cover_images/9780078022159/9780078022159_smallCoverImage.jpg)
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
![Starting Out with Python (4th Edition)](https://www.bartleby.com/isbn_cover_images/9780134444321/9780134444321_smallCoverImage.gif)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
![Digital Fundamentals (11th Edition)](https://www.bartleby.com/isbn_cover_images/9780132737968/9780132737968_smallCoverImage.gif)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
![Database System Concepts](https://www.bartleby.com/isbn_cover_images/9780078022159/9780078022159_smallCoverImage.jpg)
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
![Starting Out with Python (4th Edition)](https://www.bartleby.com/isbn_cover_images/9780134444321/9780134444321_smallCoverImage.gif)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
![Digital Fundamentals (11th Edition)](https://www.bartleby.com/isbn_cover_images/9780132737968/9780132737968_smallCoverImage.gif)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
![C How to Program (8th Edition)](https://www.bartleby.com/isbn_cover_images/9780133976892/9780133976892_smallCoverImage.gif)
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
![Database Systems: Design, Implementation, & Manag…](https://www.bartleby.com/isbn_cover_images/9781337627900/9781337627900_smallCoverImage.gif)
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
![Programmable Logic Controllers](https://www.bartleby.com/isbn_cover_images/9780073373843/9780073373843_smallCoverImage.gif)
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education