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Back propagation

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Nagarajan
Nagarajan

Revathy Karthika Nithya

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ARTIFICIAL NEURAL NETWORK
BACK PROPAGATION PRESENTED BY Karthika.T Nithya.G Revathy.R
INTRODUCTION ,[object Object],[object Object]
BACK PROPAGATION ,[object Object],[object Object],[object Object],[object Object],[object Object]
FEED FORWARD NETWORK Network activation flows in one direction only: from the input layer to the output layer, passing through the hidden layer. Each unit in a layer is connected in the forward direction to every unit in the next layer.
ARCHITECTURE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TRAINING ALGORITHM ,[object Object],[object Object],[object Object],[object Object],[object Object]
INITIALIZATION OF WEIGHTS ,[object Object],[object Object],[object Object]
FEED FORWARD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
BACK PROPAGATION OF ERRORS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
UPDATION OF WEIGHTS AND BIASES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
APPLICATION ALGORITHM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MERITS OF BACK PROPAGATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DEMERITS OF BACK PROPAGATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ROBOTICS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
BACK PROPAGATION NETWORK ,[object Object],[object Object],[object Object]
BEHAVIOR RULES ,[object Object],[object Object],[object Object],[object Object]
Lego Mindstorms robots are cool toys used by hobbyists all around the world. They prove suitable for building mobile robots and programming them with artificial intelligence.
SPACE ROBOT ,[object Object],[object Object]
TRAINING NETWORK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
[object Object],[object Object],[object Object]
NUMBER RECOGNITION SYSTEM ,[object Object],[object Object],[object Object],[object Object],[object Object]
ACQUISITION AND PREPROCESSING ,[object Object],[object Object],[object Object]
FEATURE EXTRACTION & RECOGNITION ,[object Object],[object Object],[object Object]
RECOGNITION ,[object Object],[object Object],[object Object],[object Object],[object Object]
FACE RECOGNITION SYSTEM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A. Face Image Acquisition To collect the face images, a scanner has been used. After scanning, the image can be saved into various formats such as Bitmap, JPEG, GIF and TIFF. This FRS can process face images of any format. B.Filtering and Clipping The input face of the system may contain noise and garbage data that must be removed. Filter has been used for fixing these problems. For this purpose median filtering technique has been used. After filtering, the image is clipped to obtain the necessary data that is required for removing the unnecessary background that surrounded the image.
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],E. Features Extraction To extract features of a face at first the image is converted into a binary. From this binary image the centroid (X,Y) of the face image is calculated using equation 1and 2 Where x, y is the co-ordinate values and m=f(x,y)=0 or 1. Then from the centroid, only face has been cropped and converted into the gray level and the features have been collected. F. Recognition Extracted features of the face images have been fed in to the Genetic algorithm and Back-propagation Neural Network for recognition. The unknown input face image has been recognized by Genetic Algorithm and Back-propagation Neural Network Recognition phase
RECOGNIZE FACE BY BPN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FACE RECOGNITION SYSTEM ,[object Object],1. We want to find a person within a large database of faces (e.g. in a police database). These systems typically return a list of the most likely people in the database . Often only one image is available per person. It is usually not necessary for recognition to be done in real-time. 2. We want to identify particular people in real-time (e.g. in a security monitoring system, location tracking system, etc.), or we want to allow access to a group of people and deny access to all others (e.g. access to a building, computer, etc.) [8]. Multiple images per person are often available for training and real-time recognition is required.
[object Object],[object Object],[object Object]
FINGERPRINT RECOGNITION SYSTEM
 
HARDWARE & SOFTWARE
LICENSE PLATE RECOGNITION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RECOGNITION WITH NEURAL NETWORK ,[object Object],[object Object],[object Object],[object Object],[object Object]
CITY WORD RECOGNITION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
LEAF RECOGNITION SYSTEM ,[object Object],[object Object],Another main part of this work is the integration of a feed-forward back propagation neuronal network. The inputs for this neuronal network are the individual tokens of a leaf image, and as a token normally consists of a cosines and sinus angle, the amount of input layers for this network are the amount of tokens multiplied by two. The image on the left should give you an idea of the neuronal network that takes place in the Leaves Recognition application.
LEAF RECOGNITION SYSTEM ,[object Object],[object Object],[object Object],[object Object],In order to understand the algorithm consider the figure and details shown below.
LEAF RECOGNITION SYSTEM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RECOGNITION 1.Screen to display the selected leaf1. 2.Screen to display the edge and tokens of the selected Leaf1 3.Screen to display the Leaf Image1. 4.Screen to display the results of the Recognition module. 5.Screen to display the leaf image for finding pest recognition. 6.Screen to display the Pest Percentage of the given leaf and also the damage part.
NAVIGATION OF CAR ,[object Object]
THANK YOU

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Back propagation

  • 2. BACK PROPAGATION PRESENTED BY Karthika.T Nithya.G Revathy.R
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  • 5. FEED FORWARD NETWORK Network activation flows in one direction only: from the input layer to the output layer, passing through the hidden layer. Each unit in a layer is connected in the forward direction to every unit in the next layer.
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  • 18. Lego Mindstorms robots are cool toys used by hobbyists all around the world. They prove suitable for building mobile robots and programming them with artificial intelligence.
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  • 28. A. Face Image Acquisition To collect the face images, a scanner has been used. After scanning, the image can be saved into various formats such as Bitmap, JPEG, GIF and TIFF. This FRS can process face images of any format. B.Filtering and Clipping The input face of the system may contain noise and garbage data that must be removed. Filter has been used for fixing these problems. For this purpose median filtering technique has been used. After filtering, the image is clipped to obtain the necessary data that is required for removing the unnecessary background that surrounded the image.
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  • 42. RECOGNITION 1.Screen to display the selected leaf1. 2.Screen to display the edge and tokens of the selected Leaf1 3.Screen to display the Leaf Image1. 4.Screen to display the results of the Recognition module. 5.Screen to display the leaf image for finding pest recognition. 6.Screen to display the Pest Percentage of the given leaf and also the damage part.
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