Concepts of Metaheuristics

If you want to learn a new tool or technique you must collect a list of learning resources which will be easy to read and understand,easily available or downloadable and cheap or free of cost. Similarly if you want to learn about Artificial Neural Network you must know what are the learning resources you must have in your bookshelf or laptop. A list of : 1.Books 2.Presentations 3.Videos 4.Courses that you may attend were recently published in the Baipatra home page. While preparing this the following factors were considered : 1.The list must not be extensive but selective. 2.Only the best resources will be included. 3.Cost of the same will be minimal or nothing. 4.Links will be active and usable. After considering all the above factors and conducting a search through the net and based on experience finally the list is prepared and presented to those who are really interested to apply the advantage of Artificial Neural Network in their research.

Baipatra: How to predict time dependent variables with the h... : Objective : To predict the probability of rainfall based on monthly mean data of rainfall,runoff and evapotranspiration collected from a si...

The neural networks can be classified based on direction of signal flow,training algorithms,activation functions,network topology etc. All the neural networks can be classified into Supervised,Unsupervised and Reinforcement Class based on the learning method i.e.,training procedures it has adopted.With respect to direction of signal flow such kind of models can be grouped into feed-forward and feedback sub-groups. Based on number of layers neural networks can be subdivided into Single,Multi-layer and Recurrent Neural Networks.

In ANN modeling a graph having input nodes connected to one single output nod is developed.Each node will have its own weight assigned randomly. At the output node all the weighted input nodes are summed up and activated or magnified with the help of Activation Functions. The Activation Functions can be of many types like Step,Ramp,Hypertan,Sinusoidal,Sigmoidal etc. After being activated the output is compared with the desired output(Supervised NN) or the median/mean or any other measures of a set of attributes(for making clusters;Unsupervised NN). If the output from the model is satisfactory with the objective of the model then the model is said to be learning and if not the weightage of the connections are changed and the entire process is repeated. This changing of the weightage is known as Training of the network and is conducted by various methods but mainly by Conjugate Gradient Descent,Back Propagaton and/or Quick Propagation or by some special procedures like Levenberg Marqua...