Self-creating and organizing neural networks

We have developed a self-creating and organizing unsupervised learning algorithm for artificial neural networks. In this study, we introduce SCONN and SCONN2 as two versions of self-creating and organizing neural network (SCONN) algorithms. SCONN creates an adaptive uniform vector quantizer (VQ), whereas SCONN2 creates an adaptive nonuniform VQ by neural-like architecture. SCONN´s begin with only one output node, which has a sufficiently wide activation level, and the activation level decrease depending upon the time or the activation history. SCONN´s decide automatically whether to adapt the weights of existing nodes or to create a new “son node.” They are compared with two famous algorithms-the Kohonen´s self organizing feature map (SOFM) (1988) as a neural VQ and the Linde-Buzo-Gray (LBG) algorithm (1980) as a traditional VQ. The results show that SCONN´s have significant benefits over other algorithms