Gap-based estimation: Choosing the smoothing parameters for Probabilistic and general regression neural networks

Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by crossvalidation or clustering. In this letter, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy

A Time-Domain/Frequency-Domain Simulation Tool For Microwave Component Analysis In Microwave Engineering Courses

In many applications, the capability of analyzing microwave components in both time domain and frequency domain is highly desirable. This capability is also very useful in electromagnetic/microwave education. However, the lack of a simulation tool which can perform the analysis in both domains forces engineers and students to use a combination of software and hardware and frequently switch among them. As a result, the analysis process becomes time consuming and requires a long learning curve. We developed a simulation tool that addresses this need and enables the educational study of microwave components in both domains. ©2008 IEEE

High-Q Resonators And Filters Inside Advanced Low-Temperature Co-Fired Ceramic Substrates Using Fine-Scale Periodicity

Effective media are shown to be created by patterning a material to overcome inherent material losses. Two different types of effective media, i.e., the windowpane and stratified media formed by fine-scale periodicity, are investigated. The effect of different perforations in the material is demonstrated. In particular, a great reduction in dielectric loss is observed in the stratified medium, making high-Q resonators and filters possible out of relatively lossy materials. Low-temperature co-fired ceramic (LTCC) fabrication can form advanced metamaterial substrates by realizing these two layer-by-layer effective media. High-Q (unloaded quality factors up to 762) resonators are demonstrated using the stratified medium concept, which reduces the dielectric loss by roughly 50%. Two-pole filters at $K$-band are measured to have insertions losses as low as 0.47 dB with 8.5% bandwidth. The demonstrated unloaded Q factor is much larger than that of a homogenous LTCC resonator, which is limited to an unloaded Q factor of approximately 400. © 2006 IEEE