Neural Network Design by Martin T Hagan

By Martin T Hagan

This publication, by means of the authors of the Neural community Toolbox for MATLAB, offers a transparent and particular assurance of basic neural community architectures and studying principles. In it, the authors emphasize a coherent presentation of the significant neural networks, tools for education them and their purposes to sensible difficulties. gains huge assurance of educating equipment for either feedforward networks (including multilayer and radial foundation networks) and recurrent networks. as well as conjugate gradient and Levenberg-Marquardt diversifications of the backpropagation set of rules, the textual content additionally covers Bayesian regularization and early preventing, which make sure the generalization skill of educated networks. Associative and aggressive networks, together with characteristic maps and studying vector quantization, are defined with uncomplicated construction blocks. A bankruptcy of useful education tips for functionality approximation, trend acceptance, clustering and prediction, in addition to 5 chapters providing unique real-world case stories. precise examples and diverse solved difficulties. Slides and complete demonstration software program might be downloaded from hagan.okstate.edu/nnd.html.(from amazon)

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Do you suppose that there is a combination of bias and transfer function that might allow this? i. 1 that will do the job if the bias is zero? ii. Is there a bias that will do the job if the linear transfer function is used? If yes, what is it? iii. Is there a bias that will do the job if a log-sigmoid transfer function is used? Again, if yes, what is it? iv. Is there a bias that will do the job if a symmetrical hard limit transfer function is used? Again, if yes, what is it? 4 A two-layer neural network is to have four inputs and six outputs.

Three of the most commonly used functions are discussed below. 2, sets the output of the neuron to 0 if the function argument is less than 0, or 1 if its argument is greater than or equal to 0. We will use this function to create neurons that classify inputs into two distinct categories. It will be used extensively in Chapter 4. 2 illustrates the input/output characteristic of a single-input neuron that uses a hard limit transfer function. Here we can see the effect of the weight and the bias. Note that an icon for the hard limit transfer function is shown between the two figures.

4 A single-layer neural network is to have six inputs and two outputs. The outputs are to be limited to and continuous over the range 0 to 1. What can you tell about the network architecture? Specifically: i. How many neurons are required? ii. What are the dimensions of the weight matrix? iii. What kind of transfer functions could be used? iv. Is a bias required? The problem specifications allow you to say the following about the network. i. Two neurons, one for each output, are required. ii. The weight matrix has two rows corresponding to the two neurons and six columns corresponding to the six inputs.

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