Hybrid Intelligent Systems for Pattern Recognition
We describe in this lecture new hybrid methods using soft computing techniques with application to pattern recognition problems. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. Combining SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. We consider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person.
As a prelude, we provide a brief overview of the existing methodologies for solving pattern recognition problems. We then describe our own approach in dealing with these problems. We show in this lecture that face recognition can be achieved by using modular neural networks and fuzzy logic. Genetic algorithms can also be use to optimize the architecture of the face recognition system. Fingerprint recognition can also be achieved by applying modular neural networks and fuzzy logic in a similar way as in the method for face recognition. Finally, voice recognition can be achieved by applying neural networks, fuzzy logic, and genetic algorithms. We will illustrate in this lecture each of these recognition problems and its solutions in real world situations. In each application of the SC techniques to solve a real-world pattern recognition problem, we show that the intelligent approach proves to be more efficient and accurate that traditional approaches and also offers additional advantages.