Neural Network Models.pptx.pdf

Artificial neural network models  Artificial neural network models Feedforward artificial neural networks Perceptron and Multilayer Perceptron neural networks Radial basis function artificial neural networks Recurrent neural networks Modular neural networks Feedforward artificial neur...

Artificial neural network models  Artificial neural network models Feedforward artificial neural networks Perceptron and Multilayer Perceptron neural networks Radial basis function artificial neural networks Recurrent neural networks Modular neural networks Feedforward artificial neural networks when data moves in one direction between the input and output nodes. Data moves forward through layers of nodes, and won’t cycle backwards through the same layers Perceptron and Multilayer Perceptron neural networks A Perceptron model is a binary classifier, separating data into two different classifications. Multilayer Perceptron artificial neural networks adds complexity and density, with the capacity for many hidden layers between the input and output layer.  Radial basis function artificial neural networks Radial basis function neural networks usually have an input layer, a layer with radial basis function nodes with different parameters, and an output layer. Models can be used to perform classification, regression for time series, and to control systems. Radial basis functions calculate the absolute value between a center point and a given point. In the case of classification, a radial basis function calculates the distance between an input and a learned classification. If the input is closest to a specific tag, it is classified as such.  Recurrent Neural Networks Recurrent neural networks are powerful tools when a model is designed to process sequential data. The model will move data forward and loop it backwards to previous steps in the artificial neural network to best achieve a task and improve predictions. The layers between the input and output layers are recurrent, in that relevant information is looped back and retained. Memory of outputs from a layer is looped back to the input where it is held to improve the process for the next input. Recurrent neural networks are also used within sequence to sequence models, which are used for natural language processing.  Modular neural networks A Modular artificial neural network consists of a series of networks or components that work together to achieve a task. A complex task can therefore be broken down into smaller components. If applied to data processing or the computing process, the speed of the processing will be increased as smaller components can work in tandem.