A multilayer incremental neural network architecture for classification
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A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.
KeywordsGenetic Algorithm Artificial Intelligence Complex System Feature Vector Hide Layer
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- A. S. Miller, B. H. Blott, T. K. Hames. Review of neurel network npplications in medical imaging and signal processing,Medical and Biological Engineering and Computing, vol. 30, pp. 449–464, September 1992.Google Scholar
- D. L. Prados. New learning algorithm for training multilayered neural networks that use genetic algorithm techniques,Electronics Letters, vol. 28, no. 16, pp. 1560–1562, 30 July 1992.Google Scholar
- J. Makhoul, A. El-Jaroudi, R. Schwartz. Formation of disconnected decision regions with a single hidden layer,IJCNN Int. Joint Conf. Neural Networks. vol. 1, pp. 445–460, 1989.Google Scholar
- D.E. Goldberg,Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.Google Scholar