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Hybrid intelligent system for pattern recognition

100%

Melin P. , Castillo O.

2007

tom Vol. 1, No. 2

13-19

We describe in this paper a general overview oj the analysis and design of hybrid intelligent systems for pattern recognition applications. Hybrid intelligent systems can be developed by a careful combination of several soft-computing techniques. The combination of soft computing techniques has to take advantage of the capabilities of each technique in solving port of the pattern recognition problem. We review the problems of face, fingerprint and mice recognition and their soiution using hybrid intelligent systems. Recognition rates achieved with the hybrid approaches are comparable with the best approaches known for solving these recognition problems.

Soft Computing and Fractal Theory for Intelligent Manufacturing

100%

Castillo O. , Melin P.

2007

tom Vol. 1, No. 1

41-47

We describe in this paper the combination of soft computing techniques and fractal theory for achieving intelligent manufacturing. Soft computing techniques can be used to develop hybrid intelligent systems. Fractal theory can be used to analyze the geometrical complexity of natural and artificial objects. The careful combination of soft computing and fractal theory can provide us with a good mix of intelligent techniques and fractal mathematical tools, which can help in achieving automation of manufacturing processes. We consider in this paper several manufacturing and automation problems that are efficiently solved with the proposed approach.

Response Integration in Ensemble Neural Networks using The Sugeno Integral and Fuzzy Inference System for Pattern Recognition

100%

Lopez M. , Melin P.

2008

tom Vol. 2, No. 1

52-58

Combining the outputs of multiple neural networks has been used in Ensemble architectures to improve the decision accuracy in many applications fields, including pattern recognition, in particular for the case of fingerprints. In this paper, we describe a set of experiments performed in order to find the optimal individual networks in terms of the architecture and training rule. In the second step, we used the fuzzy Sugeno Integral to integrate results of the ensemble neural networks. This method combines objective evidence in the form of the network's outputs, with subjective measures of their performance. In the third step, we used a Fuzzy Inference System for the decision process of finding the output of the ensemble neural networks, and finally a comparison of experimental results between Fuzzy Sugeno Integral and the Fuzzy Inference System are presented.

Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and its Optimization with Genetic Algorithms

80%

Hidalgo D. , Castillo O. , Melin P.

2008

tom Vol. 2, No. 1

59-73

We describe in this paper a comparative study between Fuzzy Inference Systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimized fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behavior of the two different integration methods for modular neural networks for multimodal biometry.