Concepts of Metaheuristics

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

Category Other Output Type Information/Fact TAG ANN Type of Message * Message Cover Feature ANN or Artificial Neural Network can be defined as the parallel processing units which can mimic the nervous signal of the human being for processing of information signals. It use the weights of the input processing units to create a weighted average of all the inputs.Then applies an activation function after the signal has passed through a Threshold function . Entire procedure is repeated when there is multiple layers within the input and output.Every time the signal creates an output it is compared with the observed one to differentiate between the two(error). Once the error is identified the weights are changed and again the same process is repeated( training ). When the difference between the predicted and actual/available output is within the desired limit or number of train