Concepts of Metaheuristics

This video explains the concept of Artificial Neural Networks in a very simple way. "Artificial neural networks, usually simply called neural networks, are computing systems inspired by the biological neural networks that constitute animal brains. "(Wiki). This tutorial will help you learn the basic concept and how to apply ANN to solve simple problems with examples. This tutorial is a part of a course on " Introduction to Artificial Neural Networks "

An introduction to the most used multi-criteria decision-making method including example. The tutorial uses a graphical mode to explain the concept.  The Analytic Hierarchy Process (AHP) is a method for organizing and analyzing complex decisions, using math and psychology. It was developed by Thomas L. Saaty in the 1970s and has been refined since then.( Passage Technology ). See the entire video as given above. If you are interested to know more about the technique then click here Music Courtesy: https://pixabay.com/ If you want more such tutorial visit: http://www.baipatra.ws If you have a paper to publish then consider the journals here: http://energyinstyle.website/  

A tutorial on solving Quadratic Optimization Problems

Classification of Optimization Techniques full tutorial at http://www.baipatra.ws  

Constraints in Optimization Techniques Full Tutorial: http://www.baipatra.ws

Fundamentals of Optimization Techniques Full Tutorial at http://www.baipatra.ws

A brief introduction to distribution function which depicts the pattern of data. This information is essential to select the type of ANN models that can be applied to develop a model based on that data. Access the complete course at https://gum.co/Ksjlq or find such videos at http://www.baipatra.ws Music by Palle1958 from https://pixabay.com/

Auto and Cross Regression Models

Auto and Cross-Correlation

Lear about the concepts of Linear Programming 1)How to formulate the problem ? 2)How to solve the problem by the graphical method?

Learn about Dynamic Programming with Examples.

A simple tutorial on GA. Explained with example.