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DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING
CAP 6615
Neural Networks
Rajesh Pydipati
Introduction
The objective of taking this course was to get a clear understanding of the concepts involved in neural network computing so that the technology can be tailored to solve a plethora of real world problems with wide ranging applications in various fields which are mentioned below. Emphasis was mainly on programming to observe the working of the algorithms.
Course Description
• Objectives: Understand the concepts and learn the techniques of neural network computing.
• Prerequisites: A familiarity with basic concepts in calculus, linear algebra, and probability theory. Calculus requirements include: differentiation, chain rule, integration. Linear algebra requirements include: matrix multiplication, inverse, pseudo-inverse.
• Main topics: Introduction to neural computational models including classification, association, optimization, and self-organization. Learning and discovery. Knowledge-based neural network design and algorithms.
• Applications include: pattern recognition, expert systems, control, signal analysis, and computer vision.
Syllabus
• Basic neural computational models
• Feedforward networks
• Learning / back propagation
• Association networks
• Classification
• Self-Organization
• Radial Basis Function networks
• Support Vector Machines
• Networks based on lattice computation
• Applications
Projects:
A set of four projects were done as part of this course. A detailed description of each project with an approach to the solution is presented next.
Project 1:
Problem statement:
Project 1a: Implement the SLP learning algorithm. Implement the algorithm yourselves; do not use any ANN package. Train your SLP to classify the capital letter patterns A, B, C, and D, in two classes, C1 and -C1 as follows: A belongs to C1; B, C, and D all belong to -C1. After training, test whether your SLP correctly classifies the same four patterns. You may use either the unipolar or the bipolar version of the patterns
Approach:
This problem was to make us understand the basic working of a neural network circuit, the ‘perceptron’.
The problem involved, was to identify four different letter patterns which were fed as a stream of ‘0’s and ‘1’s to the network. After constructing the network architecture, it was trained on some data. After training was complete, some patterns were tested to test the efficacy of the algorithm. Algorithms were written in MATLAB.
Results:
The network was able to perfectly classify the four letter patterns
Project 2a: Implement an SLP to solve the following problem:
1. Randomly choose 1000 points on either side of the line y = 0.5x + 2. Do not choose points exactly on the line. Also, pick the points between fixed bounds b1 ................
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