Solutions Manual

Neural Network Design (2nd Edition)

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This is not a completed Solutions Manual. In case you need help with any exercise of the book or generally you have a question about Neural Networks you can have a look at Artificial Intelligence Stack Exchange, which is the best community to learn and discuss. You are also welcome to use discussions of this repository.

Book details

Title : Neural Network Design (2nd Edition)
Authors : Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesus
ISBN-10 : 0-9717321-1-6
ISBN-13 : 978-0-9717321-1-7
A PDF version of this textbook can be found at : http://hagan.okstate.edu/NNDesign.pdf

Chapters included :

Chapter	Name
2	Neuron Model and Network Architectures
4	Perceptron Learning Rule
7	Supervised Hebbian Learning
8	Performance Surfaces and Optimum Points
9	Performance Optimization
10	Widrow-Hoff Learning
11	Backpropagation
12	Variations on Backpropagation
13	Generalization
14	Dynamic Networks (DNN)
15	Associative Learning
16	Competitive Networks (CNN)
17	Radial Basis Networks (RBF)

🔶 Note that some solutions contain Greek language text , you can either ignore it or use Google Translate .

Included solutions :

Chapter	Exercise	Add Date	Update Date	Author(s)
2	E2.6	01/17/20	01/17/20	@estamos
4	E4.8	01/17/20	01/17/20	@estamos
7	E7.1 E7.2 E7.4 E7.5 E7.6 E7.9	03/04/20	14/01/21	@estamos & @OUStudent
8	E8.1 E8.2 E8.4 E8.7 E8.10	01/16/21	01/16/21	@OUStudent
9	E9.1 E9.5 E7.7 E9.10	01/18/21	01/20/21	@OUStudent
10	E10.2 E10.4 E10.5 E10.6 E10.12	01/17/20	01/20/21	@estamos & @OUStudent
11	E11.1 E11.3 E11.6 E11.7 E11.9 E11.10 E11.11 E11.12 E11.13 E11.25	01/17/20	01/25/21	@estamos & @OUStudent
12	E12.2 E12.4 E12.7 E12.9 E12.11	01/17/20	01/25/20	@estamos & @OUStudent
13	E13.3 E13.5 13.13	02/12/21	02/12/21	@OUStudent
15	E15.1 E15.5 15.9	02/12/21	02/12/21	@OUStudent
16	E16.3 E16.5 E16.10 E16.13	01/17/20	02/12/21	@estamos & @OUStudent
17	E17.3 E17.5 E17.10 E17.11	01/17/20	02/12/21	@estamos & @OUStudent

More solutions available:

🔶 Note that for many exercises below enumeration is based on the 1st edition book .

Title : Neural Network Design
Authors : Martin T. Hagan, Howard B. Demuth, Mark H. Beale
ISBN : 978-0-534-94332-5
Publishing Company, Boston, MA, 1996

Exercises	Download
E2.2	webpage
E2.3	webpage
E3.1	doc
E4.2 E4.3 E4.4 E4.5 E4.6 E4.8	webpage
E4.3 E4.8	doc
E8.5 E9.2 E9.6	doc
E10.4 E10.5 E11.7 E11.11	doc
E12.1 E12.4 E12.5 E12.6	doc
E13.5 E14.2 E14.4 E14.8	doc
E15.6 E15.7 E14.4 E14.8	doc
E16.1 E16.3 E16.5 E16.7	doc

Relative webpages

• Introduction to Neural Networks and Applications - Fall 2001 - Missouri University of Science and Technology

• Index of above webpage

• Chapter 2 to 4 Notes and exercises by @jtcass01

Demos

This is a set of demonstrations paired with the Neural Network Design & Neural Network Design: Deep Learning books written in Python. You can read more about nndesigndemos at PyPI of project.

Authors : Amir Jafari, Martin Hagan, Pedro Uría

Installation

pip install nndesigndemos

Virtual environment (recommended)

python3 -m venv env
source env/bin/activate  # macOS/Linux
env\Scripts\activate.bat  # Windows
pip install nndesigndemos

Dependencies

• Python 3.5+

• PyQt5 5.14.1

• NumPy 1.18.1

• SciPy 1.4.1

• Matplotlib 3.1.2

Usage

from nndesigndemos import nndtoc
nndtoc()