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Adaptive Pattern Classification Using Fuzzy ARTMAP
Han, Soowhan,Jeon, Do-Hong 한국경영과학회 1994 한국경영과학회 학술대회논문집 Vol.- No.2
We have investigated the fuzzy ARTMAP neural network architecture to solve pattern classification problems. ARTMAP is a class of neural network architecture that perform incremental supervised learning of recognition categories and multidimensional maps in response to input vectors presented in arbitrary order. Fuzzy ARTMAP is a generalized ARTMAP to deal with analog or binary input vectors. This generalization accomplished by replacing the ART1 modules of the binary ARTMAP with fuzzy ART modules. Fuzzy ARTMAP is easy to use and differs from many previous fuzzy pattern recognition algorithms which perform off-line optimization of a criterion function. The one we have used for the evaluation in this research is that of the two-dimensional binary XOR gate problem, generalized to real-valued two-dimensional vectors. The performance of the fuzzy ARTMAP is compared with Nearest Neighbor pattern classification, decision surface mapping methods(DSM), and a two-layer perceptron trained by error back propagation. The fuzzy ARTMAP outperforms these methods with respect to error rates and the number of prototypes required to describe class boundaries.
A New Hybrid Genetic Algorithm for Nonlinear Channel Blind Equalization
Soowhan Han,Imgeun Lee,Changwook Han 한국지능시스템학회 2004 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.4 No.3
In this study, a hybrid genetic algorithm merged with simulated annealing is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated one, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.