Exact Classification with Two-Layered Perceptrons

We study the capabilities of two-layered perceptrons for classifying exactly a given subset. Both necessary and sufficient conditions are derived for subsets to be exactly classifiable with two-layered perceptrons that use the hard-limiting response function. The necessary conditions can be viewed as generalizations of the linear-separability condition of one-layered perceptrons and confirm the conjecture that the capabilities of two-layered perceptrons are more limited than those of three-layered perceptrons. The sufficient conditions show that the capabilities of two-layered perceptrons extend beyond the exact classification of convex subsets. Furthermore, we present an algorithmic approach to the problem of verifying the sufficiency condition for a given subset

On-line lot-sizing with perceptrons

x+167hlm.;24c

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Configurable Low Power Analog Multilayer Perceptron

A configurable, low power analog implementation of a multilayer perceptron (MLP) is presented in this work. It features a highly programmable system that allows the user to create a MLP neural network design of their choosing. In addition to the configurability, this neural network provides the ability of low power operation via analog circuitry in its neurons. The main MLP system is made up of 12 neurons that can be configurable to any number of layers and neurons per layer until all available resources are utilized. The MLP network is fabricated in a standard 0.13 μm CMOS process occupying approximately 1 mm2 of on-chip area. The MLP system is analyzed at several different configurations with all achieving a greater than 1 Tera-operations per second per Watt figure of merit. This work offers a high speed, low power, and scalable alternative to digital configurable neural networks