I would like to know the basic details of this algorithm. I found various papers but failed in understanding the actual algorithm.
Thank you in advance.
I was working on this in my dissertation and I found this source really interesting, because it includes source code and details.
http://natureofcode.com/book/chapter-10-neural-networks/
It is very basic, but you cand fin here as well into the codeproject another one:
http://www.codeproject.com/Articles/16419/AI-Neural-Network-for-beginners-Part-of
Good luck!
I recommend this book:
Despite it was pusblished in 1993, the second chapter contains all the fundamentals about the Neuron model and Network achitecture, includind on layer models and multiple layer networks.
It is very useful to introduce using the Neural Network toolbox of matlab because it simplifies a lot of internal issues related to the implementation.
There are also some examples (view the link) that can help you understand basic concepts.
In
Fabrício P. Härter, Haroldo Fraga de Campos Velho, New approach to applying neural network in nonlinear dynamic model, Applied Mathematical Modelling, Volume 32, Issue 12, December 2008, Pages 2621-2633,
an example of application for data assimilation purposes is included. You may find how they choose the number of neurons as well as a justification of using multilayer neural network.
Cheers.
http://es.mathworks.com/help/nnet/examples.html
Book Neural Network Toolbox 6: Users Guide
i think matlab is a good tool .. where you can direct learn from help..
If you have access "Machine Learning" (Tom M. Mitchell, McGraw-Hill, 1997), chapter 4 has a rigorous and simple to understand introduction.
Herewith I have attached a note which I have prepared using some textbooks and some online materials.
You may use matlab to understand the examples
Hello everyone,
Thanks a lot for your response.
I will go through each article, paper, presentation and book shared by you all. Surely these will help me.
Hi, the following papers can help you:
https://www.researchgate.net/publication/312141846_A_projection_type_steepest_descent_neural_network_for_solving_a_class_of_nonsmooth_optimization_problems
https://www.researchgate.net/publication/324531026_An_efficient_one-layer_recurrent_neural_network_for_solving_a_class_of_nonsmooth_pseudoconvex_optimization_problems
https://www.researchgate.net/publication/283448488_A_non-penalty_recurrent_neural_network_for_solving_a_class_of_constrained_optimization_problems
https://www.researchgate.net/publication/236899596_A_New_Steepest_Descent_Differential_Inclusion-Based_Method_for_Solving_General_Nonsmooth_Convex_Optimization_Problems
https://www.researchgate.net/publication/236899619_A_differential_inclusion-based_approach_for_solving_nonsmooth_convex_optimization_problems
https://www.researchgate.net/publication/236180856_A_recurrent_neural_network_for_solving_a_class_of_generalized_convex_optimization_problems
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