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A self-constructed radial basis function neural network and its applications

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2 Author(s)
Chun-Liang Hou ; Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan ; Shie-Jue Lee

A method of generating various shapes of kernel functions, such as rectangular shapes and Gaussian-like shapes, for constructing neural networks is proposed. Our method can dynamically adjust the kernel shapes to match the desired output. For example, a kernel function of rectangular shape can be generated for a desired output which has a constant value for some interval. Determination of the initial prototypes may greatly affect the performance of neural networks. Most papers use trial-and-error methods to determine the initial prototypes. We incorporate the ART algorithm to construct the initial prototypes of neural networks. We also define a measure based on entropy theory to detect the local shape of the desired output. The goal of the measurement is to generate suitable kernel shapes. Therefore, there is a critical difference between traditional methods and ours. In other words, our scheme can construct proper shapes, nodes and initial weights automatically. Experimental results show that our method has a better performance than traditional methods

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Systems, Man, and Cybernetics, 2000 IEEE International Conference on  (Volume:5 )

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