Concepts of Metaheuristics

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...

Types of Neural Networks :  There are two different types of neural network based on the learning mechanism - Supervised and Unsupervised Neural Networks where in case of the former a desired dataset is provided("teacher") with which the model predicted data is compared to find level of accuracy of the model.The Unsupervised neural network will try to identify the inherent properties(data mining) of the input dataset with the help of different unsupervised learning methods. Based on the signal transmission through the neural networks,neural network models can be divided into three distinct classes : Feed forward - where the signal is transmitted only in the forward direction(Input-Hidden-Output); Feedback - a feedback or error correction signal is transmitted back to the source so that deviation from the desired data is actuated and Recurrent - signal is transmitted both ways.Recurrent Neural Networks can use their internal memory to process a...