by Valluru B. Rao
M&T Books, IDG Books Worldwide, Inc.
ISBN: 1558515526 Pub Date: 06/01/95
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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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Anthony Mason, Nicholas Sweeney, and Ronald Baer studied the neural network approach in two laboratory experiments and one field experiment, in diagnosing faults in circuit boards.
Test point readings were expressed as one vector. A fault vector was also defined with elements representing possible faults. The two vectors became a training pair. Backpropagation was used.
Gary Wasserman and Agus Sudjianto model the prediction of warranty claims with neural networks. The nonlinearity in the data prompted this approach.
The motivation for the study comes from the need to assess warranty costs for a company that offers extended warranties for its products. This is another application that uses backpropagation. The architecture used was 2-10-1.
J. Nellis and T. Stonham developed a neural network character recognition system that adapts dynamically to a writing style.
They use a hybrid neural network for hand-printed character recognition, that integrates image processing and neural network architectures. The neural network uses random access memory (RAM) to model the functionality of an individual neuron. The authors use a transform called the five-way image processing transform on the input image, which is of size 32x32 pixels. The transform converts the high spatial frequency data in a character into four low frequency representations. What they achieve by this are position invariance, and a ratio of black to white pixels approaching 1, rotation invariance, and capability to detect and correct breaks within characters. The transformed data are input to the neural network that is used as a classifier and is called a discriminator.
A particular writing style that has less variability and therefore fewer subclasses is needed to classify the style. Network size will also reduce confusion, and conflicts lessen.
Optical character recognition (OCR) is one of the most successful commercial applications of neural networks. Caere Corporation brought out its neural network product in 1992, after studying more than 100,000 examples of fax documents. Caere’s AnyFax technology combines neural networks with expert systems to extract character information from Fax or scanned images. Calera, another OCR vendor, started using neural networks in 1984 and also benefited from using a very large (more than a million variations of alphanumeric characters) training set.
A neural network architecture called ART-EMAP (Gail Carpenter and William Ross) integrates Adaptive Resonance Theory (ART) with spatial and temporal evidence integration for predictive mapping (EMAP). The result is a system capable of complex 3-D object recognition. A vision system that samples two-dimensional perspectives of a three-dimensional object is created that results in 98% correct recognition with an average of 9.2 views presented on noiseless test data, and 92% recognition with an average of 11.2 views presented on noisy test data. The ART-EMAP system is an extension of ARTMAP, which is a neural network architecture that performs supervised learning of recognition categories and multidimensional maps in response to input vectors. A fuzzy logic extension of ARTMAP is called Fuzzy ARTMAP, which incorporates two fuzzy modules in the ART system.
A sampling of current research and commercial applications with neural networks and fuzzy logic technology is presented. Neural networks are applied toward a wide variety of problems, from aiding medical diagnosis to detecting circuit faults in printed circuit board manufacturing. Some of the problem areas where neural networks and fuzzy logic have been successfully applied are:
The use of fuzzy logic and neural networks in software and hardware systems can only increase!
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