| C++ Neural Networks and Fuzzy Logic |
by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95
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Compared to all other neural network models, Fukushimas Neocognitron is more complex and ambitious. It demonstrates the advantages of a multilayered network. The Neocognitron is one of the best models for recognizing handwritten symbols. Many pairs of layers called the S layer, for simple layer, and C layer, for complex layer, are used. Within each S layer are several planes containing simple cells. Similarly, there are within each C layer, an equal number of planes containing complex cells. The input layer does not have this arrangement and is like an input layer in any other neural network.
The number of planes of simple cells and of complex cells within a pair of S and C layers being the same, these planes are paired, and the complex plane cells process the outputs of the simple plane cells. The simple cells are trained so that the response of a simple cell corresponds to a specific portion of the input image. If the same part of the image occurs with some distortion, in terms of scaling or rotation, a different set of simple cells responds to it. The complex cells output to indicate that some simple cell they correspond to did fire. While simple cells respond to what is in a contiguous region in the image, complex cells respond on the basis of a larger region. As the process continues to the output layer, the C-layer component of the output layer responds, corresponding to the entire image presented in the beginning at the input layer.
ART1 is the first model for adaptive resonance theory for neural networks developed by Gail Carpenter and Stephen Grossberg. This theory was developed to address the stabilityplasticity dilemma. The network is supposed to be plastic enough to learn an important pattern. But at the same time it should remain stable when, in short-term memory, it encounters some distorted versions of the same pattern.
ART1 model has A and B field neurons, a gain, and a reset as shown in Figure 5.8. There are top-down and bottom-up connections between neurons of fields A and B. The neurons in field B have lateral connections as well as recurrent connections. That is, every neuron in this field is connected to every other neuron in this field, including itself, in addition to the connections to the neurons in field A. The external input (or bottom-up signal), the top-down signal, and the gain constitute three elements of a set, of which at least two should be a +1 for the neuron in the A field to fire. This is what is termed the two-thirds rule. Initially, therefore, the gain would be set to +1. The idea of a single winner is also employed in the B field. The gain would not contribute in the top-down phase; actually, it will inhibit. The two-thirds rule helps move toward stability once resonance, or equilibrium, is obtained. A vigilance parameter ρ is used to determine the parameter reset. Vigilance parameter corresponds to what degree the resonating category can be predicted. The part of the system that contains gain is called the attentional subsystem, whereas the rest, the part that contains reset, is termed the orienting subsystem. The top-down activity corresponds to the orienting subsystem, and the bottom-up activity relates to the attentional subsystem.
Figure 5.8 The ART1 network.
In ART1, classification of an input pattern in relation to stored patterns is attempted, and if unsuccessful, a new stored classification is generated. Training is unsupervised. There are two versions of training: slow and fast. They differ in the extent to which the weights are given the time to reach their eventual values. Slow training is governed by differential equations, and fast training by algebraic equations.
ART2 is the analog counterpart of ART1, which is for discrete cases. These are self-organizing neural networks, as you can surmise from the fact that training is present but unsupervised. The ART3 model is for recognizing a coded pattern through a parallel search, and is developed by Carpenter and Grossberg. It tries to emulate the activities of chemical transmitters in the brain during what can be construed as a parallel search for pattern recognition.
The basic concepts of neural network layers, connections, weights, inputs, and outputs have been discussed. An example of how adding another layer of neurons in a network can solve a problem that could not be solved without it is given in detail. A number of neural network models are introduced briefly. Learning and training, which form the basis of neural network behavior has not been included here, but will be discussed in the following chapter.
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