We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.

The radiation properties of oscillating electric dipoles are studied theoretically for three and four layered systems including a single metallic slab based on angular spectrum representation of vector spherical waves. One of the remarkable results obtained is the transmission energy spectrum showing strong dependence on the thickness of a dielectric layer placed between oscillating electric dipole and metallic surface, which explains the experimental results of molecular fluorescence into surface plasmon modes. The theory based on angular spectrum representation and tunneling current provides us with a clear identification of plasmonic excitation transfer, transmission loss associated with plasmon transport in metallic layer, and energy dissipation or quenching of excitation due to surface plasmon excitation at the metallic surface in relation to the characteristic complex wave number of evanescent waves.