Other forms of XXX Sample Clauses

Other forms of XXX. There has been an exponential growth of types of ANNs that have been developed during the last thirty years, each with modifications which enable them to adapt to particular scenarios (see Table 1). The first modification to the MLP came in the form of a feed forward ANNs which requires that all nodes are still connected, but adds an extra layer of hidden nodes between input and output, with training through backpropagation (111). The structure is essential the same as a MLP and hence often categorised together with them. Radial Basis function networks have the same structure as a feed forward MLP but uses a radial function, rather than a logistic function. They are better suited for continuous data, such as signal and image data. Deep feed forward networks started to introduce multiple hidden layers within the networks, exponentially increasing both the complexity and simultaneously the processing power and time needed to train them (111). Recurrent Neural Networks (43) were the first to enable ANNs to learn context, wherein certain cells within the network would have the function to be fed back in their previous output from prior iterations. This was especially beneficial for computer text reading, where prior words would have a decision-making bearing on subsequent word choice. Various other adaptations followed, often by bolting various types of networks together depending on the required situation. Deep convolutional Networks include hidden layers which can compact and simplify data, other layers that look for interconnectivity and finally into feed forward networks which classify answers. These networks are most commonly for processing of images where the network will analyse an image one pixel at a time (79,82,112).