The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

Abstract. In this work, we propose a dynamic, flexible and interactive urban digital terrain platform with spatial data and query processing capabilities of geographic information systems, multimedia database functionality and graphical modeling infrastructure. A new data element, called Geo-Node, which stores image, spatial data and 3-D CAD objects is developed using an efficient data structure. The system effectively handles data transfer of Geo-Nodes between main memory and secondary storage with an optimized directional replacement policy (DRP) based buffer management scheme. Polyhedron structures are used in digital surface modeling and smoothing process is performed by interpolation. The experimental results show that our framework achieves high performance and works effectively with urban scenes independent from the amount of spatial data and image size. The proposed platform may contribute to the development of various applications such as Web GIS systems based on 3-D graphics standards (e.g., X3-D and VRML) and services which integrate multi-dimensional spatial information and satellite/aerial imagery.

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