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

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.