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Additional info for Matlab - Neural Network Toolbox User's Guide
2-2 All of the neurons in this toolbox have provision for a bias, and a bias is used in many of our examples and will be assumed in most of this toolbox. However, you may omit a bias in a neuron if you want. As previously noted, the bias b is an adjustable (scalar) parameter of the neuron. It is not an input. ” Transfer Functions Many transfer functions are included in this toolbox. A complete list of them can be found in “Transfer Function Graphs” in Chapter 14. Three of the most commonly used functions are shown below.
First consider a single layer of neurons. A Layer of Neurons A one-layer network with R input elements and S neurons follows. Input Layer of Neurons n w1, 1 1 p1 a f 1 Where... b 1 1 p2 p n a 2 2 f R = number of elements in input vector b 3 2 1 pR nS wS, R f aS S = number of neurons in layer bS 1 a= f (Wp + b) In this network, each element of the input vector p is connected to each neuron input through the weight matrix W. The ith neuron has a summer that gathers its weighted inputs and bias to form its own scalar output n(i).
Simulation With Concurrent Inputs in a Static Network . Simulation With Sequential Inputs in a Dynamic Network Simulation With Concurrent Inputs in a Dynamic Network . 2-14 . 2-14 . 2-15 . 2-17 Training Styles . . . . . . . . . . 2-20 Incremental Training (of Adaptive and Other Networks) . 2-20 Batch Training . . . . . . . . . . 2-22 Summary . . . . . . . . . . . 2-27 Figures and Equations . . . . . . . . 2-28 2 Neuron Model and Network Architectures Neuron Model Simple Neuron A neuron with a single scalar input and no bias appears on the left below.