A temporal neural network model for constructing connectionist expert system knowledge bases

This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.


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In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab®. The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means...
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Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
Citation Formats
F. N. Alpaslan, “A temporal neural network model for constructing connectionist expert system knowledge bases,” JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, pp. 119–133, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48652.