ISE member 2The present study reveals the formation of porous anodic films on titanium at an increased growth rate in hot phosphate/glycerol electrolyte by reducing the water content. A porous titanium oxide film of 12 µm thickness, with a relatively low content of phosphorus species, is developed after anodizing at 5 V for 3.6 ks in 0.6 mol dm -3 K 2 HPO 4 +0.2 mol dm -3 K 3 PO 4 /glycerol electrolyte containing only 0.04%water at 433 K. The growth efficiency is reduced by increasing the formation voltage to 20 V, due to formation of crystalline oxide, which induces gas generation during anodizing. The film formed at 20 V consists of two layers, with an increased concentration of phosphorus species in the inner layer. The outer layer, comprising approximately 25% of the film thickness, is developed at low formation voltages, of less than 10 V, during the initial anodizing at a constant current density of 250 A m -2. The pore diameter is not significantly dependent upon the formation voltage, being ~10 nm.

This paper proposes a two-functional network in which adaptive methods are implemented for sophisticated recognition and clustering. In the first subnetwork, self-organized clustering is realized. The clustering is based on Mahalanobis distance. The result of the first subnetwork becomes a vector of similarity values between a given input pattern and all patterns of cluster nodes. The second subnetwork determines the optimum label from the similarity vector. The second network consists of nodes associated with specific labels. All connections between the label nodes of the second functional network and the cluster nodes of the first functional network are determined by supervised learning. Every calculation is executed in parallel and pipelined forms. In addition, the proposed network is shown to provide good performance by some experiments. In particular, this report shows that handwriting letters can be accurately recognized by using this network.